Summary The Senior Data Integration Engineer is responsible for the design, development, and operation of the data integration and ingestion processes that deliver partner and internal data into our analytics environment. The role owns the flow of data from source acquisition through the curated data warehouse tables consumed by reporting platforms, operational systems, and business analysts. This is a senior, hands-on engineering position spanning the full integration lifecycle: acquiring data from a wide range of external partner and internal systems, validating and conditioning that data on arrival, transforming it into the structures that support analysis and operations, and operating those processes reliably in production. The role is concerned equally with building new integrations and with the continued performance, accuracy, and timeliness of those already in service. A central objective of the position is to advance reusable, well-instrumented integration patterns that shorten the time required to onboard new data sources and that improve the reliability and transparency of data delivery to the business. The Senior Data Integration Engineer will also contribute substantially to the planned modernization of our data platform, evaluating and recommending tooling, architecture, and migration approach for leadership consideration. The role sets technical direction and development standards for data integration work and collaborates closely with data architects, business analysts, stakeholders across the organization, and the technical contacts of our external data partners. Essential Duties and Responsibilities This list of duties and responsibilities is not all inclusive and may be expanded to include other duties and responsibilities as management may deem necessary from time to time. Design, develop, and maintain data integration processes that acquire data from partner and internal sources, including flat file transfers over SFTP, REST API endpoints, and direct database connections. Develop reusable, configuration-driven ingestion patterns that reduce the effort and elapsed time required to onboard new partner data feeds. Develop and maintain the T-SQL transformation logic that carries data from landing and staging layers through to the curated warehouse tables supporting reporting, operational systems, and analyst queries. Design ingestion processes to be idempotent and safely re-runnable, incorporating automated retry and restart behavior for failed executions. Implement automated validation and quarantine processes so that records failing business-defined quality rules are isolated, reported, and prevented from reaching downstream consumers. Implement data quality rules defined by the business, including schema validation, reconciliation, row count and threshold checks, and anomaly detection. Establish monitoring, logging, and alerting for pipeline execution state, data freshness, and load completion, and automate the communication of ingestion status to stakeholders. Diagnose and resolve production data incidents, determine root cause, coordinate remediation, and document preventive measures through runbooks and post-incident review. Contribute to dimensional data model design in collaboration with the data architect and senior team members. Establish and maintain version control, code review, and repeatable deployment practices for database and pipeline code. Define and uphold development standards for data integration work through code review and technical guidance. Evaluate and recommend tooling, architecture, and sequencing for the platform modernization effort for leadership consideration. Migrate established integration workflows to modernized patterns incrementally and without disruption to production operations. Maintain documentation of data feeds, dependencies, lineage, ownership, and escalation paths. Perform all work involving protected health information in accordance with HIPAA requirements, including least-privilege access, secure transmission and storage of partner data, and the exclusion of PHI from logs and non-production environments. Coordinate with partner technical contacts, as needed, to resolve file format, schema, and connectivity questions. Provide occasional off-hours support for critical data load failures or production support rotations as needed to support timely response to critical data issues. Support AI and machine learning initiatives by maintaining reliable, secure, and well-governed data pipelines and datasets used for model development, testing, deployment, monitoring, and ongoing performance evaluation. Maintain confidentiality of information processed & follow company policies and procedures. Qualifications Requires six (6) or more years of professional data engineering, data operations, data platform operations, or related experience. Bachelor's degree in Computer Science, Engineering, Information Systems, or related field, or equivalent professional experience preferred. Experience leading teams and developing supervisory staff preferred. To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. Other qualifications include: Advanced T-SQL development skills, including: Set-based rewriting of row-by-row and cursor-based logic. MERGE, upsert, and slowly changing dimension load patterns. Window functions and complex analytic queries. Execution plan analysis, index strategy, statistics management, and resolution of performance issues such as parameter sniffing. Transaction management and structured error handling within stored procedures, including correct rollback behavior on partial failure. Demonstrated proficiency in Python for data engineering applications, including API-based data acquisition, file parsing and format handling, data validation, and the development of packaged, scheduled jobs. Familiarity with common data libraries such as requests and pandas is expected. Demonstrated experience acquiring and integrating data from heterogeneous sources, including delimited, fixed-width, JSON, and XML file formats; REST APIs requiring authentication, pagination, and rate-limit handling; and direct database connectivity. Proficiency with Git and collaborative development workflows, including branching, pull requests, and code review. A code-first development approach, with integration logic authored and maintained in T-SQL and Python under source control. Ability to analyze pipeline and query performance and to improve the reliability, scalability, and cost efficiency of data workloads. Strong written communication skills, with the ability to produce runbooks, technical documentation, and incident reports, and to convey the business impact of technical issues to non-technical stakeholders. Strong problem-solving skills, attention to detail, and demonstrated ownership of production systems. Experience with Microsoft Azure data services such as Azure Data Factory or Microsoft Fabric, or comparable cloud orchestration platforms. Experience migrating on-premises SQL Server integration workloads to a cloud platform. Experience with dimensional modeling and data warehouse design. Experience designing and rationalizing SQL Server Agent job dependencies and scheduling. Experience establishing version control, code review, and repeatable deployment practices for database code. Experience implementing CI/CD pipelines for database projects. Experience handling protected health information under HIPAA, or comparably regulated data under an equivalent framework preferred. Experience with healthcare or pharmacy data, including claims, eligibility, prescription, or delivery data preferred. Familiarity with data governance, metadata management, and data lineage practices. Physical Demands The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. (The phrases "occasionally," "regularly," and "frequently" correspond to the following definitions: "Occasionally" means up to 1/3 of working time, "regularly" means between 1/3 and 2/3 of working time, and "frequently" means 2/3 and more working time.) While performing the duties of this job, the employee is frequently required to sit; talk or hear; and use hands to handle, or touch objects or controls. The employee is regularly required to stand and walk. On occasion the incumbent may be required to stoop, bend or reach above the shoulders. The employee would rarely need to lift up to 25 pounds. Specific vision abilities required by this job include close vision, distance vision, color vision, peripheral vision, depth perception, and ability to adjust focus. Work Environment The position is a hybrid position with 2 - 3 days office presence in Milwaukee, WI required. The colleague may perform work-related travel on rare occasions (less than 10%) with an emphasis on travel for impact. . click apply for full job details
09/30/2026
Full time
Summary The Senior Data Integration Engineer is responsible for the design, development, and operation of the data integration and ingestion processes that deliver partner and internal data into our analytics environment. The role owns the flow of data from source acquisition through the curated data warehouse tables consumed by reporting platforms, operational systems, and business analysts. This is a senior, hands-on engineering position spanning the full integration lifecycle: acquiring data from a wide range of external partner and internal systems, validating and conditioning that data on arrival, transforming it into the structures that support analysis and operations, and operating those processes reliably in production. The role is concerned equally with building new integrations and with the continued performance, accuracy, and timeliness of those already in service. A central objective of the position is to advance reusable, well-instrumented integration patterns that shorten the time required to onboard new data sources and that improve the reliability and transparency of data delivery to the business. The Senior Data Integration Engineer will also contribute substantially to the planned modernization of our data platform, evaluating and recommending tooling, architecture, and migration approach for leadership consideration. The role sets technical direction and development standards for data integration work and collaborates closely with data architects, business analysts, stakeholders across the organization, and the technical contacts of our external data partners. Essential Duties and Responsibilities This list of duties and responsibilities is not all inclusive and may be expanded to include other duties and responsibilities as management may deem necessary from time to time. Design, develop, and maintain data integration processes that acquire data from partner and internal sources, including flat file transfers over SFTP, REST API endpoints, and direct database connections. Develop reusable, configuration-driven ingestion patterns that reduce the effort and elapsed time required to onboard new partner data feeds. Develop and maintain the T-SQL transformation logic that carries data from landing and staging layers through to the curated warehouse tables supporting reporting, operational systems, and analyst queries. Design ingestion processes to be idempotent and safely re-runnable, incorporating automated retry and restart behavior for failed executions. Implement automated validation and quarantine processes so that records failing business-defined quality rules are isolated, reported, and prevented from reaching downstream consumers. Implement data quality rules defined by the business, including schema validation, reconciliation, row count and threshold checks, and anomaly detection. Establish monitoring, logging, and alerting for pipeline execution state, data freshness, and load completion, and automate the communication of ingestion status to stakeholders. Diagnose and resolve production data incidents, determine root cause, coordinate remediation, and document preventive measures through runbooks and post-incident review. Contribute to dimensional data model design in collaboration with the data architect and senior team members. Establish and maintain version control, code review, and repeatable deployment practices for database and pipeline code. Define and uphold development standards for data integration work through code review and technical guidance. Evaluate and recommend tooling, architecture, and sequencing for the platform modernization effort for leadership consideration. Migrate established integration workflows to modernized patterns incrementally and without disruption to production operations. Maintain documentation of data feeds, dependencies, lineage, ownership, and escalation paths. Perform all work involving protected health information in accordance with HIPAA requirements, including least-privilege access, secure transmission and storage of partner data, and the exclusion of PHI from logs and non-production environments. Coordinate with partner technical contacts, as needed, to resolve file format, schema, and connectivity questions. Provide occasional off-hours support for critical data load failures or production support rotations as needed to support timely response to critical data issues. Support AI and machine learning initiatives by maintaining reliable, secure, and well-governed data pipelines and datasets used for model development, testing, deployment, monitoring, and ongoing performance evaluation. Maintain confidentiality of information processed & follow company policies and procedures. Qualifications Requires six (6) or more years of professional data engineering, data operations, data platform operations, or related experience. Bachelor's degree in Computer Science, Engineering, Information Systems, or related field, or equivalent professional experience preferred. Experience leading teams and developing supervisory staff preferred. To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. Other qualifications include: Advanced T-SQL development skills, including: Set-based rewriting of row-by-row and cursor-based logic. MERGE, upsert, and slowly changing dimension load patterns. Window functions and complex analytic queries. Execution plan analysis, index strategy, statistics management, and resolution of performance issues such as parameter sniffing. Transaction management and structured error handling within stored procedures, including correct rollback behavior on partial failure. Demonstrated proficiency in Python for data engineering applications, including API-based data acquisition, file parsing and format handling, data validation, and the development of packaged, scheduled jobs. Familiarity with common data libraries such as requests and pandas is expected. Demonstrated experience acquiring and integrating data from heterogeneous sources, including delimited, fixed-width, JSON, and XML file formats; REST APIs requiring authentication, pagination, and rate-limit handling; and direct database connectivity. Proficiency with Git and collaborative development workflows, including branching, pull requests, and code review. A code-first development approach, with integration logic authored and maintained in T-SQL and Python under source control. Ability to analyze pipeline and query performance and to improve the reliability, scalability, and cost efficiency of data workloads. Strong written communication skills, with the ability to produce runbooks, technical documentation, and incident reports, and to convey the business impact of technical issues to non-technical stakeholders. Strong problem-solving skills, attention to detail, and demonstrated ownership of production systems. Experience with Microsoft Azure data services such as Azure Data Factory or Microsoft Fabric, or comparable cloud orchestration platforms. Experience migrating on-premises SQL Server integration workloads to a cloud platform. Experience with dimensional modeling and data warehouse design. Experience designing and rationalizing SQL Server Agent job dependencies and scheduling. Experience establishing version control, code review, and repeatable deployment practices for database code. Experience implementing CI/CD pipelines for database projects. Experience handling protected health information under HIPAA, or comparably regulated data under an equivalent framework preferred. Experience with healthcare or pharmacy data, including claims, eligibility, prescription, or delivery data preferred. Familiarity with data governance, metadata management, and data lineage practices. Physical Demands The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. (The phrases "occasionally," "regularly," and "frequently" correspond to the following definitions: "Occasionally" means up to 1/3 of working time, "regularly" means between 1/3 and 2/3 of working time, and "frequently" means 2/3 and more working time.) While performing the duties of this job, the employee is frequently required to sit; talk or hear; and use hands to handle, or touch objects or controls. The employee is regularly required to stand and walk. On occasion the incumbent may be required to stoop, bend or reach above the shoulders. The employee would rarely need to lift up to 25 pounds. Specific vision abilities required by this job include close vision, distance vision, color vision, peripheral vision, depth perception, and ability to adjust focus. Work Environment The position is a hybrid position with 2 - 3 days office presence in Milwaukee, WI required. The colleague may perform work-related travel on rare occasions (less than 10%) with an emphasis on travel for impact. . click apply for full job details
Summary The Senior Data Integration Engineer is responsible for the design, development, and operation of the data integration and ingestion processes that deliver partner and internal data into our analytics environment. The role owns the flow of data from source acquisition through the curated data warehouse tables consumed by reporting platforms, operational systems, and business analysts. This is a senior, hands-on engineering position spanning the full integration lifecycle: acquiring data from a wide range of external partner and internal systems, validating and conditioning that data on arrival, transforming it into the structures that support analysis and operations, and operating those processes reliably in production. The role is concerned equally with building new integrations and with the continued performance, accuracy, and timeliness of those already in service. A central objective of the position is to advance reusable, well-instrumented integration patterns that shorten the time required to onboard new data sources and that improve the reliability and transparency of data delivery to the business. The Senior Data Integration Engineer will also contribute substantially to the planned modernization of our data platform, evaluating and recommending tooling, architecture, and migration approach for leadership consideration. The role sets technical direction and development standards for data integration work and collaborates closely with data architects, business analysts, stakeholders across the organization, and the technical contacts of our external data partners. Essential Duties and Responsibilities This list of duties and responsibilities is not all inclusive and may be expanded to include other duties and responsibilities as management may deem necessary from time to time. Design, develop, and maintain data integration processes that acquire data from partner and internal sources, including flat file transfers over SFTP, REST API endpoints, and direct database connections. Develop reusable, configuration-driven ingestion patterns that reduce the effort and elapsed time required to onboard new partner data feeds. Develop and maintain the T-SQL transformation logic that carries data from landing and staging layers through to the curated warehouse tables supporting reporting, operational systems, and analyst queries. Design ingestion processes to be idempotent and safely re-runnable, incorporating automated retry and restart behavior for failed executions. Implement automated validation and quarantine processes so that records failing business-defined quality rules are isolated, reported, and prevented from reaching downstream consumers. Implement data quality rules defined by the business, including schema validation, reconciliation, row count and threshold checks, and anomaly detection. Establish monitoring, logging, and alerting for pipeline execution state, data freshness, and load completion, and automate the communication of ingestion status to stakeholders. Diagnose and resolve production data incidents, determine root cause, coordinate remediation, and document preventive measures through runbooks and post-incident review. Contribute to dimensional data model design in collaboration with the data architect and senior team members. Establish and maintain version control, code review, and repeatable deployment practices for database and pipeline code. Define and uphold development standards for data integration work through code review and technical guidance. Evaluate and recommend tooling, architecture, and sequencing for the platform modernization effort for leadership consideration. Migrate established integration workflows to modernized patterns incrementally and without disruption to production operations. Maintain documentation of data feeds, dependencies, lineage, ownership, and escalation paths. Perform all work involving protected health information in accordance with HIPAA requirements, including least-privilege access, secure transmission and storage of partner data, and the exclusion of PHI from logs and non-production environments. Coordinate with partner technical contacts, as needed, to resolve file format, schema, and connectivity questions. Provide occasional off-hours support for critical data load failures or production support rotations as needed to support timely response to critical data issues. Support AI and machine learning initiatives by maintaining reliable, secure, and well-governed data pipelines and datasets used for model development, testing, deployment, monitoring, and ongoing performance evaluation. Maintain confidentiality of information processed & follow company policies and procedures. Qualifications Requires six (6) or more years of professional data engineering, data operations, data platform operations, or related experience. Bachelor's degree in Computer Science, Engineering, Information Systems, or related field, or equivalent professional experience preferred. Experience leading teams and developing supervisory staff preferred. To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. Other qualifications include: Advanced T-SQL development skills, including: Set-based rewriting of row-by-row and cursor-based logic. MERGE, upsert, and slowly changing dimension load patterns. Window functions and complex analytic queries. Execution plan analysis, index strategy, statistics management, and resolution of performance issues such as parameter sniffing. Transaction management and structured error handling within stored procedures, including correct rollback behavior on partial failure. Demonstrated proficiency in Python for data engineering applications, including API-based data acquisition, file parsing and format handling, data validation, and the development of packaged, scheduled jobs. Familiarity with common data libraries such as requests and pandas is expected. Demonstrated experience acquiring and integrating data from heterogeneous sources, including delimited, fixed-width, JSON, and XML file formats; REST APIs requiring authentication, pagination, and rate-limit handling; and direct database connectivity. Proficiency with Git and collaborative development workflows, including branching, pull requests, and code review. A code-first development approach, with integration logic authored and maintained in T-SQL and Python under source control. Ability to analyze pipeline and query performance and to improve the reliability, scalability, and cost efficiency of data workloads. Strong written communication skills, with the ability to produce runbooks, technical documentation, and incident reports, and to convey the business impact of technical issues to non-technical stakeholders. Strong problem-solving skills, attention to detail, and demonstrated ownership of production systems. Experience with Microsoft Azure data services such as Azure Data Factory or Microsoft Fabric, or comparable cloud orchestration platforms. Experience migrating on-premises SQL Server integration workloads to a cloud platform. Experience with dimensional modeling and data warehouse design. Experience designing and rationalizing SQL Server Agent job dependencies and scheduling. Experience establishing version control, code review, and repeatable deployment practices for database code. Experience implementing CI/CD pipelines for database projects. Experience handling protected health information under HIPAA, or comparably regulated data under an equivalent framework preferred. Experience with healthcare or pharmacy data, including claims, eligibility, prescription, or delivery data preferred. Familiarity with data governance, metadata management, and data lineage practices. Physical Demands The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. (The phrases "occasionally," "regularly," and "frequently" correspond to the following definitions: "Occasionally" means up to 1/3 of working time, "regularly" means between 1/3 and 2/3 of working time, and "frequently" means 2/3 and more working time.) While performing the duties of this job, the employee is frequently required to sit; talk or hear; and use hands to handle, or touch objects or controls. The employee is regularly required to stand and walk. On occasion the incumbent may be required to stoop, bend or reach above the shoulders. The employee would rarely need to lift up to 25 pounds. Specific vision abilities required by this job include close vision, distance vision, color vision, peripheral vision, depth perception, and ability to adjust focus. Work Environment The position is a hybrid position with 2 - 3 days office presence in Milwaukee, WI required. The colleague may perform work-related travel on rare occasions (less than 10%) with an emphasis on travel for impact. . click apply for full job details
09/30/2026
Full time
Summary The Senior Data Integration Engineer is responsible for the design, development, and operation of the data integration and ingestion processes that deliver partner and internal data into our analytics environment. The role owns the flow of data from source acquisition through the curated data warehouse tables consumed by reporting platforms, operational systems, and business analysts. This is a senior, hands-on engineering position spanning the full integration lifecycle: acquiring data from a wide range of external partner and internal systems, validating and conditioning that data on arrival, transforming it into the structures that support analysis and operations, and operating those processes reliably in production. The role is concerned equally with building new integrations and with the continued performance, accuracy, and timeliness of those already in service. A central objective of the position is to advance reusable, well-instrumented integration patterns that shorten the time required to onboard new data sources and that improve the reliability and transparency of data delivery to the business. The Senior Data Integration Engineer will also contribute substantially to the planned modernization of our data platform, evaluating and recommending tooling, architecture, and migration approach for leadership consideration. The role sets technical direction and development standards for data integration work and collaborates closely with data architects, business analysts, stakeholders across the organization, and the technical contacts of our external data partners. Essential Duties and Responsibilities This list of duties and responsibilities is not all inclusive and may be expanded to include other duties and responsibilities as management may deem necessary from time to time. Design, develop, and maintain data integration processes that acquire data from partner and internal sources, including flat file transfers over SFTP, REST API endpoints, and direct database connections. Develop reusable, configuration-driven ingestion patterns that reduce the effort and elapsed time required to onboard new partner data feeds. Develop and maintain the T-SQL transformation logic that carries data from landing and staging layers through to the curated warehouse tables supporting reporting, operational systems, and analyst queries. Design ingestion processes to be idempotent and safely re-runnable, incorporating automated retry and restart behavior for failed executions. Implement automated validation and quarantine processes so that records failing business-defined quality rules are isolated, reported, and prevented from reaching downstream consumers. Implement data quality rules defined by the business, including schema validation, reconciliation, row count and threshold checks, and anomaly detection. Establish monitoring, logging, and alerting for pipeline execution state, data freshness, and load completion, and automate the communication of ingestion status to stakeholders. Diagnose and resolve production data incidents, determine root cause, coordinate remediation, and document preventive measures through runbooks and post-incident review. Contribute to dimensional data model design in collaboration with the data architect and senior team members. Establish and maintain version control, code review, and repeatable deployment practices for database and pipeline code. Define and uphold development standards for data integration work through code review and technical guidance. Evaluate and recommend tooling, architecture, and sequencing for the platform modernization effort for leadership consideration. Migrate established integration workflows to modernized patterns incrementally and without disruption to production operations. Maintain documentation of data feeds, dependencies, lineage, ownership, and escalation paths. Perform all work involving protected health information in accordance with HIPAA requirements, including least-privilege access, secure transmission and storage of partner data, and the exclusion of PHI from logs and non-production environments. Coordinate with partner technical contacts, as needed, to resolve file format, schema, and connectivity questions. Provide occasional off-hours support for critical data load failures or production support rotations as needed to support timely response to critical data issues. Support AI and machine learning initiatives by maintaining reliable, secure, and well-governed data pipelines and datasets used for model development, testing, deployment, monitoring, and ongoing performance evaluation. Maintain confidentiality of information processed & follow company policies and procedures. Qualifications Requires six (6) or more years of professional data engineering, data operations, data platform operations, or related experience. Bachelor's degree in Computer Science, Engineering, Information Systems, or related field, or equivalent professional experience preferred. Experience leading teams and developing supervisory staff preferred. To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. Other qualifications include: Advanced T-SQL development skills, including: Set-based rewriting of row-by-row and cursor-based logic. MERGE, upsert, and slowly changing dimension load patterns. Window functions and complex analytic queries. Execution plan analysis, index strategy, statistics management, and resolution of performance issues such as parameter sniffing. Transaction management and structured error handling within stored procedures, including correct rollback behavior on partial failure. Demonstrated proficiency in Python for data engineering applications, including API-based data acquisition, file parsing and format handling, data validation, and the development of packaged, scheduled jobs. Familiarity with common data libraries such as requests and pandas is expected. Demonstrated experience acquiring and integrating data from heterogeneous sources, including delimited, fixed-width, JSON, and XML file formats; REST APIs requiring authentication, pagination, and rate-limit handling; and direct database connectivity. Proficiency with Git and collaborative development workflows, including branching, pull requests, and code review. A code-first development approach, with integration logic authored and maintained in T-SQL and Python under source control. Ability to analyze pipeline and query performance and to improve the reliability, scalability, and cost efficiency of data workloads. Strong written communication skills, with the ability to produce runbooks, technical documentation, and incident reports, and to convey the business impact of technical issues to non-technical stakeholders. Strong problem-solving skills, attention to detail, and demonstrated ownership of production systems. Experience with Microsoft Azure data services such as Azure Data Factory or Microsoft Fabric, or comparable cloud orchestration platforms. Experience migrating on-premises SQL Server integration workloads to a cloud platform. Experience with dimensional modeling and data warehouse design. Experience designing and rationalizing SQL Server Agent job dependencies and scheduling. Experience establishing version control, code review, and repeatable deployment practices for database code. Experience implementing CI/CD pipelines for database projects. Experience handling protected health information under HIPAA, or comparably regulated data under an equivalent framework preferred. Experience with healthcare or pharmacy data, including claims, eligibility, prescription, or delivery data preferred. Familiarity with data governance, metadata management, and data lineage practices. Physical Demands The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodation may be made to enable individuals with disabilities to perform the essential functions. (The phrases "occasionally," "regularly," and "frequently" correspond to the following definitions: "Occasionally" means up to 1/3 of working time, "regularly" means between 1/3 and 2/3 of working time, and "frequently" means 2/3 and more working time.) While performing the duties of this job, the employee is frequently required to sit; talk or hear; and use hands to handle, or touch objects or controls. The employee is regularly required to stand and walk. On occasion the incumbent may be required to stoop, bend or reach above the shoulders. The employee would rarely need to lift up to 25 pounds. Specific vision abilities required by this job include close vision, distance vision, color vision, peripheral vision, depth perception, and ability to adjust focus. Work Environment The position is a hybrid position with 2 - 3 days office presence in Milwaukee, WI required. The colleague may perform work-related travel on rare occasions (less than 10%) with an emphasis on travel for impact. . click apply for full job details
What you will do The AI Vision Systems Specialist is responsible for designing, developing, and deploying computer vision solutions that enable intelligent automation and quality control in Clarios manufacturing plants. This role supports the Future Factory vision by integrating advanced vision systems and AI algorithms into production lines. The specialist works closely with IT, product engineering, operations, and regional teams to deliver scalable, robust, and innovative solutions for automated inspection and process optimization. A key part of this role is connecting with business stakeholders across regions to collaboratively identify and align on pain points and priorities. The specialist will develop proof-of-concept (PoC) solutions in lab environments, validate them, and drive the integration of core solutions into base machines for global deployment. This position is remote with possible relocation required. How you will do it Business Engagement & Needs Identification Proactively engage with business and plant teams across regions to understand operational challenges, pain points, and improvement opportunities. Collaborate with business owners to create product roadmaps that prioritize delivery of key capabilities to the plants. Facilitate workshops and collaborative sessions to define priorities for vision system applications Solution Development & Deployment Design, develop, and implement computer vision systems for automated inspection, defect detection, and process monitoring. Develop PoC solutions in lab environments, validate with stakeholders, and drive integration into base machines. Integrate vision systems (edge devices, cameras, sensors, lighting) with manufacturing equipment. Create predictive and preventative maintenance schedule along with necessary spare parts list to ensure long-term viability of solutions. Collaborate with IT and engineering to deploy AI models and data pipelines for real-time analytics. Regional Execution & Scalability Lead and support deployment of vision solutions across multiple regions, ensuring adaptability to local requirements and standards. Coordinate with regional teams for pilot builds, R&D trials, commissioning, and feedback loops. Technical Leadership & Documentation Evaluate and select hardware/software for vision applications in brownfield and greenfield scenarios. Develop and maintain technical documentation, including Supplier Statements of Work (SSOW), Bills of Materials (BOM), System calibration and quality procedures, and spare parts lists. Guide vendor selection and manage strategic partnerships with equipment suppliers and integrators. Continuous Improvement & Innovation Stay current with emerging technologies in computer vision, machine learning, and industrial automation. Conduct benchmarking and share findings with global teams. Champion innovation and standardization across sites and regions. Communication & Collaboration Communicate technical concepts and project status to stakeholders at all levels, including executive leadership. Foster cross-functional and cross-regional collaboration to ensure alignment and best practice sharing. To succeed in this role, the individual must have a sound knowledge of vision systems in addition to internal business acumen: Visionary thinking and technical leadership in computer vision for manufacturing. Strong business acumen and ability to translate operational needs into technical solutions. Innovation and adaptability in fast-paced, evolving environments. Effective communication and collaboration across functions, cultures, and regions. Structured planning, project management, and follow-through. What we look for Required Bachelor's or Master's degree in Computer Science, Electrical Engineering, Robotics, or related field. 3+ years of experience in computer vision, machine learning, or automation engineering. Experience with vision system integration in manufacturing or industrial environments. Familiarity with industrial cameras, sensors, and lighting systems. Experience deploying AI models to edge devices or cloud platforms. Proficiency in 3D CAD tools and simulation software is a plus. Experience leading cross-functional projects in international settings. FUTURE RELOCATION TO TEXAS IS REQUIRED Applicants must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future; Clarios will not sponsor applicants for U.S. work visas for this opportunity. What you get: Medical, dental and vision care coverage and a 401(k) savings plan with company matching - all starting on date of hire Tuition reimbursement, perks, and discounts Parental and caregiver leave programs All the usual benefits such as paid time off, flexible spending, short-and long-term disability, basic life insurance, business travel insurance, and Employee Assistance Program Global market strength and worldwide market share leadership HQ location earns LEED certification for sustainability plus a full-service cafeteria and workout facility Clarios has been recognized as one of 2026's Most Ethical Companies by Ethisphere. This prestigious recognition marks the fourth consecutive year Clarios has received this distinction. Who we are: Clarios is the force behind the world's most recognizable car battery brands, powering vehicles from leading automakers like Ford, General Motors, Toyota, Honda, and Nissan. With 18,000 employees worldwide, we develop, manufacture, and distribute energy storage solutions while recovering, recycling, and reusing up to 99% of battery materials-setting the standard for sustainability in our industry. At Clarios, we're not just making batteries; we're shaping the future of sustainable transportation. Join our mission to innovate, push boundaries, and make a real impact. Discover your potential at Clarios-where your power meets endless possibilities. Veterans/Military Spouses: We value the leadership, adaptability, and technical expertise developed through military service. At Clarios, those capabilities thrive in an environment built on grit, ingenuity, and passion-where you can grow your career while helping to power progress worldwide. All qualified applicants will be considered without regard to protected characteristics. Equal Employment Opportunity: We recognize that people come with a wealth of experience and talent beyond just the technical requirements of a job. If your experience is close to what you see listed here, please apply. Diversity of experience and skills combined with passion is key to challenging the status quo. Therefore, we encourage people from all backgrounds to apply to our positions. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, status as a protected veteran or other protected characteristics protected by law. As a federal contractor, we are committed to not discriminating against any applicant or employee based on these protected statuses. We will also take affirmative action to ensure equal employment opportunities. Please let us know if you require accommodations during the interview process by emailing . We are an Equal Opportunity Employer and value diversity in our teams in terms of work experience, area of expertise, and all characteristics protected by laws in the countries where we operate. For more information on our commitment to sustainability, diversity, and equal opportunity, please read our latest report . We want you to know your rights because EEO is the law. A Note to Job Applicants: please be aware of scams being perpetrated through the Internet and social media platforms. Clarios will never require a job applicant to pay money as part of the application or hiring process. To All Recruitment Agencies: Clarios does not accept unsolicited agency resumes/CVs. Please do not forward resumes/CVs to our careers email addresses, Clarios employees or any other company location. Clarios is not responsible for any fees related to unsolicited resumes/CVs.
09/30/2026
Full time
What you will do The AI Vision Systems Specialist is responsible for designing, developing, and deploying computer vision solutions that enable intelligent automation and quality control in Clarios manufacturing plants. This role supports the Future Factory vision by integrating advanced vision systems and AI algorithms into production lines. The specialist works closely with IT, product engineering, operations, and regional teams to deliver scalable, robust, and innovative solutions for automated inspection and process optimization. A key part of this role is connecting with business stakeholders across regions to collaboratively identify and align on pain points and priorities. The specialist will develop proof-of-concept (PoC) solutions in lab environments, validate them, and drive the integration of core solutions into base machines for global deployment. This position is remote with possible relocation required. How you will do it Business Engagement & Needs Identification Proactively engage with business and plant teams across regions to understand operational challenges, pain points, and improvement opportunities. Collaborate with business owners to create product roadmaps that prioritize delivery of key capabilities to the plants. Facilitate workshops and collaborative sessions to define priorities for vision system applications Solution Development & Deployment Design, develop, and implement computer vision systems for automated inspection, defect detection, and process monitoring. Develop PoC solutions in lab environments, validate with stakeholders, and drive integration into base machines. Integrate vision systems (edge devices, cameras, sensors, lighting) with manufacturing equipment. Create predictive and preventative maintenance schedule along with necessary spare parts list to ensure long-term viability of solutions. Collaborate with IT and engineering to deploy AI models and data pipelines for real-time analytics. Regional Execution & Scalability Lead and support deployment of vision solutions across multiple regions, ensuring adaptability to local requirements and standards. Coordinate with regional teams for pilot builds, R&D trials, commissioning, and feedback loops. Technical Leadership & Documentation Evaluate and select hardware/software for vision applications in brownfield and greenfield scenarios. Develop and maintain technical documentation, including Supplier Statements of Work (SSOW), Bills of Materials (BOM), System calibration and quality procedures, and spare parts lists. Guide vendor selection and manage strategic partnerships with equipment suppliers and integrators. Continuous Improvement & Innovation Stay current with emerging technologies in computer vision, machine learning, and industrial automation. Conduct benchmarking and share findings with global teams. Champion innovation and standardization across sites and regions. Communication & Collaboration Communicate technical concepts and project status to stakeholders at all levels, including executive leadership. Foster cross-functional and cross-regional collaboration to ensure alignment and best practice sharing. To succeed in this role, the individual must have a sound knowledge of vision systems in addition to internal business acumen: Visionary thinking and technical leadership in computer vision for manufacturing. Strong business acumen and ability to translate operational needs into technical solutions. Innovation and adaptability in fast-paced, evolving environments. Effective communication and collaboration across functions, cultures, and regions. Structured planning, project management, and follow-through. What we look for Required Bachelor's or Master's degree in Computer Science, Electrical Engineering, Robotics, or related field. 3+ years of experience in computer vision, machine learning, or automation engineering. Experience with vision system integration in manufacturing or industrial environments. Familiarity with industrial cameras, sensors, and lighting systems. Experience deploying AI models to edge devices or cloud platforms. Proficiency in 3D CAD tools and simulation software is a plus. Experience leading cross-functional projects in international settings. FUTURE RELOCATION TO TEXAS IS REQUIRED Applicants must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future; Clarios will not sponsor applicants for U.S. work visas for this opportunity. What you get: Medical, dental and vision care coverage and a 401(k) savings plan with company matching - all starting on date of hire Tuition reimbursement, perks, and discounts Parental and caregiver leave programs All the usual benefits such as paid time off, flexible spending, short-and long-term disability, basic life insurance, business travel insurance, and Employee Assistance Program Global market strength and worldwide market share leadership HQ location earns LEED certification for sustainability plus a full-service cafeteria and workout facility Clarios has been recognized as one of 2026's Most Ethical Companies by Ethisphere. This prestigious recognition marks the fourth consecutive year Clarios has received this distinction. Who we are: Clarios is the force behind the world's most recognizable car battery brands, powering vehicles from leading automakers like Ford, General Motors, Toyota, Honda, and Nissan. With 18,000 employees worldwide, we develop, manufacture, and distribute energy storage solutions while recovering, recycling, and reusing up to 99% of battery materials-setting the standard for sustainability in our industry. At Clarios, we're not just making batteries; we're shaping the future of sustainable transportation. Join our mission to innovate, push boundaries, and make a real impact. Discover your potential at Clarios-where your power meets endless possibilities. Veterans/Military Spouses: We value the leadership, adaptability, and technical expertise developed through military service. At Clarios, those capabilities thrive in an environment built on grit, ingenuity, and passion-where you can grow your career while helping to power progress worldwide. All qualified applicants will be considered without regard to protected characteristics. Equal Employment Opportunity: We recognize that people come with a wealth of experience and talent beyond just the technical requirements of a job. If your experience is close to what you see listed here, please apply. Diversity of experience and skills combined with passion is key to challenging the status quo. Therefore, we encourage people from all backgrounds to apply to our positions. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, status as a protected veteran or other protected characteristics protected by law. As a federal contractor, we are committed to not discriminating against any applicant or employee based on these protected statuses. We will also take affirmative action to ensure equal employment opportunities. Please let us know if you require accommodations during the interview process by emailing . We are an Equal Opportunity Employer and value diversity in our teams in terms of work experience, area of expertise, and all characteristics protected by laws in the countries where we operate. For more information on our commitment to sustainability, diversity, and equal opportunity, please read our latest report . We want you to know your rights because EEO is the law. A Note to Job Applicants: please be aware of scams being perpetrated through the Internet and social media platforms. Clarios will never require a job applicant to pay money as part of the application or hiring process. To All Recruitment Agencies: Clarios does not accept unsolicited agency resumes/CVs. Please do not forward resumes/CVs to our careers email addresses, Clarios employees or any other company location. Clarios is not responsible for any fees related to unsolicited resumes/CVs.
Why USAA? At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the choice for the military community and their families. Embrace a fulfilling career at USAA, where our core values - honesty, integrity, loyalty and service - define how we treat each other and our members. Be part of what truly makes us special and impactful. We are proud to support active-duty military spouses. USAA roles may offer remote or hybrid flexibility for active-duty military spouses consistent with applicable policy and business needs. The Opportunity As a dedicated AI Data Solutions Scientist in the Technology organization at USAA, you will work within our innovative Data Science team and collaborate cross-functionally with our architecture, engineering, and product partners to transform our operations and experiences while producing actionable insights to drive our association forward. As a member of our dynamic community of problem-solvers, you will tackle a broad and evolving spectrum of business targets to provide outstanding impacts for our membership through scaled solutions and cloud technologies, leveraging both structured and unstructured data through traditional pillars of operations research such as simulation, optimization, and machine-learning techniques, as well as a heavy emphasis on cutting-edge technologies with generative AI, large/small language models, and advanced agent frameworks. This team is the backbone of the next generation of AI modeling at USAA, and we hope you join us on the frontier! We offer a flexible work environment that requires an individual to be in the office 4 days per week. This position can be based in one of the following locations: San Antonio, TX, Plano, TX, or Phoenix, AZ. Relocation assistance is not available for this position. What you'll do: Gather, interpret, and manipulate structured and unstructured data to enable advanced analytical solutions for the business. Develop scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value. Select the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs. Develop and deploy models within the Model Development Control (MDC) and Model Risk Management (MRM) framework. Composes, and assists peers with composing, technical documents for knowledge persistence, risk management, and technical review audiences. Assess business needs to propose/recommend analytical and modeling projects to add business value. Work with business and analytics leaders to prioritize analytics and modeling problems/research efforts. Build and maintain a robust library of reusable, production-quality algorithms and supporting code, to ensure model development and research efforts are transparent and based on the highest quality data. Translate complex business request(s) into specific analytical questions, executes on the analysis and/or modeling, and then communicates outcomes to non-technical business colleagues with focus on business action and recommendations. Manage project milestones, risks, and impediments. Escalates potential issues that could limit project success or implementation. Develop best practices for engaging with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and model risk management standards. Maintain expertise and awareness of cutting-edge techniques. Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies. Serve as a mentor to junior data scientists in modeling, analytics, and computer science tasks. Participate in internal communities that drive the maintenance and transformation of data science technologies and culture. Ensure risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures. What you have: Bachelor's degree in mathematics, Computer Science, Statistics, Science, Engineering, or quantitative field; OR 4 years of relevant education and/or experience; and 6+ years of experience in a predictive analytics or data analysis OR Advanced Degree (e.g., Master's, PhD) in mathematics, computer science, statistics, science and engineering, ai, or other similar quantitative discipline and 4+ years of experience in predictive analytics or data analysis. 4+ years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models. 4+ years of experience in Python for performing statical analysis and/or building and scoring AI/ML models Experience writing code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency). Strong experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc. Demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics and understanding real-world constraints such as latency, cost, and reliability in AI solution designs. Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts across risk stripes, including experience in the documentation and statistical validation of models for risk management. Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc. Expertise in LLMs and agentic systems development with frameworks such as LangChain/LangGraph, AgentCore, VertexAI, MCP, or others, with proven experience including prompt engineering, tuning and post-training techniques, multi-agent systems, agent optimization and tool use, RAG and context optimization, and observability and monitoring. MLOps Integration experience in facilitating engineering implementation of production scaled AI solutions in partnership with dedicated AI Engineers in cloud environments such as AWS or GCP. Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results. Experience guiding and mentoring junior technical staff in business interactions and model building. What sets you apart: Financial services, insurance, banking, or other highly regulated industry experience. Experience with cloud-native application development and modernization initiatives. US military experience through military service or a military spouse/domestic partner Compensation range: The salary range for this position is: $143,320 - $273,930. USAA does not provide visa sponsorship for this role. Please do not apply for this role if at any time (now or in the future) you will need immigration support (i.e., H-1B, TN, STEM OPT Training Plans, etc.). Compensation: USAA has an effective process for assessing market data and establishing ranges to ensure we remain competitive. You are paid within the salary range based on your experience and market data of the position. The actual salary for this role may vary by location. Employees may be eligible for pay incentives based on overall corporate and individual performance and at the discretion of the USAA Board of Directors. The above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job. Benefits: At USAA our employees enjoy best-in-class benefits to support their physical, financial, and emotional wellness. These benefits include comprehensive medical, dental and vision plans, 401(k), pension, life insurance, parental benefits, adoption assistance, paid time off program with paid holidays plus 16 paid volunteer hours, and various wellness programs. Additionally, our career path planning and continuing education assists employees with their professional goals. For more details on our outstanding benefits, visit our benefits page on Applications for this position are accepted on an ongoing basis, this posting will remain open until the position is filled. Thus, interested candidates are encouraged to apply the same day they view this posting. USAA is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
09/30/2026
Full time
Why USAA? At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the choice for the military community and their families. Embrace a fulfilling career at USAA, where our core values - honesty, integrity, loyalty and service - define how we treat each other and our members. Be part of what truly makes us special and impactful. We are proud to support active-duty military spouses. USAA roles may offer remote or hybrid flexibility for active-duty military spouses consistent with applicable policy and business needs. The Opportunity As a dedicated AI Data Solutions Scientist in the Technology organization at USAA, you will work within our innovative Data Science team and collaborate cross-functionally with our architecture, engineering, and product partners to transform our operations and experiences while producing actionable insights to drive our association forward. As a member of our dynamic community of problem-solvers, you will tackle a broad and evolving spectrum of business targets to provide outstanding impacts for our membership through scaled solutions and cloud technologies, leveraging both structured and unstructured data through traditional pillars of operations research such as simulation, optimization, and machine-learning techniques, as well as a heavy emphasis on cutting-edge technologies with generative AI, large/small language models, and advanced agent frameworks. This team is the backbone of the next generation of AI modeling at USAA, and we hope you join us on the frontier! We offer a flexible work environment that requires an individual to be in the office 4 days per week. This position can be based in one of the following locations: San Antonio, TX, Plano, TX, or Phoenix, AZ. Relocation assistance is not available for this position. What you'll do: Gather, interpret, and manipulate structured and unstructured data to enable advanced analytical solutions for the business. Develop scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value. Select the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs. Develop and deploy models within the Model Development Control (MDC) and Model Risk Management (MRM) framework. Composes, and assists peers with composing, technical documents for knowledge persistence, risk management, and technical review audiences. Assess business needs to propose/recommend analytical and modeling projects to add business value. Work with business and analytics leaders to prioritize analytics and modeling problems/research efforts. Build and maintain a robust library of reusable, production-quality algorithms and supporting code, to ensure model development and research efforts are transparent and based on the highest quality data. Translate complex business request(s) into specific analytical questions, executes on the analysis and/or modeling, and then communicates outcomes to non-technical business colleagues with focus on business action and recommendations. Manage project milestones, risks, and impediments. Escalates potential issues that could limit project success or implementation. Develop best practices for engaging with Data Engineering and IT to deploy production-ready analytical assets consistent with modeling best practices and model risk management standards. Maintain expertise and awareness of cutting-edge techniques. Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies. Serve as a mentor to junior data scientists in modeling, analytics, and computer science tasks. Participate in internal communities that drive the maintenance and transformation of data science technologies and culture. Ensure risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures. What you have: Bachelor's degree in mathematics, Computer Science, Statistics, Science, Engineering, or quantitative field; OR 4 years of relevant education and/or experience; and 6+ years of experience in a predictive analytics or data analysis OR Advanced Degree (e.g., Master's, PhD) in mathematics, computer science, statistics, science and engineering, ai, or other similar quantitative discipline and 4+ years of experience in predictive analytics or data analysis. 4+ years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models. 4+ years of experience in Python for performing statical analysis and/or building and scoring AI/ML models Experience writing code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency). Strong experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc. Demonstrated skill in performing ad-hoc analytics using descriptive, diagnostic, and inferential statistics and understanding real-world constraints such as latency, cost, and reliability in AI solution designs. Ability to assess and articulate regulatory implications and expectations of distinct modeling efforts across risk stripes, including experience in the documentation and statistical validation of models for risk management. Advanced experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc. Advanced experience with the concepts and technologies associated with unsupervised modeling such as k-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc. Expertise in LLMs and agentic systems development with frameworks such as LangChain/LangGraph, AgentCore, VertexAI, MCP, or others, with proven experience including prompt engineering, tuning and post-training techniques, multi-agent systems, agent optimization and tool use, RAG and context optimization, and observability and monitoring. MLOps Integration experience in facilitating engineering implementation of production scaled AI solutions in partnership with dedicated AI Engineers in cloud environments such as AWS or GCP. Experience communicating analytical and modeling results to non-technical business partners with emphasis on business recommendations and actionable applications of results. Experience guiding and mentoring junior technical staff in business interactions and model building. What sets you apart: Financial services, insurance, banking, or other highly regulated industry experience. Experience with cloud-native application development and modernization initiatives. US military experience through military service or a military spouse/domestic partner Compensation range: The salary range for this position is: $143,320 - $273,930. USAA does not provide visa sponsorship for this role. Please do not apply for this role if at any time (now or in the future) you will need immigration support (i.e., H-1B, TN, STEM OPT Training Plans, etc.). Compensation: USAA has an effective process for assessing market data and establishing ranges to ensure we remain competitive. You are paid within the salary range based on your experience and market data of the position. The actual salary for this role may vary by location. Employees may be eligible for pay incentives based on overall corporate and individual performance and at the discretion of the USAA Board of Directors. The above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job. Benefits: At USAA our employees enjoy best-in-class benefits to support their physical, financial, and emotional wellness. These benefits include comprehensive medical, dental and vision plans, 401(k), pension, life insurance, parental benefits, adoption assistance, paid time off program with paid holidays plus 16 paid volunteer hours, and various wellness programs. Additionally, our career path planning and continuing education assists employees with their professional goals. For more details on our outstanding benefits, visit our benefits page on Applications for this position are accepted on an ongoing basis, this posting will remain open until the position is filled. Thus, interested candidates are encouraged to apply the same day they view this posting. USAA is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Job Title: Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Visa : USC, GC, EAD (Only W2, No Sponsorship) Responsibilities Optimize and maintain large-scale feature engineering pipelines using PySpark, Pandas, and PyArrow on Hadoop-based infrastructure. Refactor and modularize ML codebases to enhance reusability, maintainability, and performance. Collaborate with platform teams on compute capacity planning, resource allocation, and system upgrades. Integrate with existing model serving frameworks to support testing, deployment, and rollback processes. Monitor and troubleshoot production ML pipelines, ensuring high reliability, low latency, and cost efficiency. Contribute to internal ML platforms by sharing insights, proposing improvements, and documenting best practices. Build near real-time ML pipelines using Kafka and Spark Streaming. Work with AWS and SageMaker MLOps ecosystem. Requirements 6+ years of experience in software engineering, data engineering, or MLOps roles. Strong programming expertise in Python, with hands-on experience in Pandas, PySpark, and PyArrow. Deep understanding of the Hadoop ecosystem, distributed computing, and performance tuning. Experience with CI/CD pipelines and best practices in ML environments. Hands-on experience with monitoring tools for ML pipeline health and performance. Strong collaboration skills with experience working in cross-functional teams (platform, data science, engineering). Experience contributing to or building internal MLOps frameworks/platforms. Familiarity with SLURM clusters or other distributed job schedulers. Exposure to Kafka, Spark Streaming, or other real-time data processing technologies. Understanding of ML lifecycle management, including versioning, deployment, and drift detection. - KB1 Ref: Pittsburgh
09/30/2026
Full time
Job Description Job Description Job Title: Machine Learning Operations Engineer Location: Dallas, Texas Type: Contract To Hire Visa : USC, GC, EAD (Only W2, No Sponsorship) Responsibilities Optimize and maintain large-scale feature engineering pipelines using PySpark, Pandas, and PyArrow on Hadoop-based infrastructure. Refactor and modularize ML codebases to enhance reusability, maintainability, and performance. Collaborate with platform teams on compute capacity planning, resource allocation, and system upgrades. Integrate with existing model serving frameworks to support testing, deployment, and rollback processes. Monitor and troubleshoot production ML pipelines, ensuring high reliability, low latency, and cost efficiency. Contribute to internal ML platforms by sharing insights, proposing improvements, and documenting best practices. Build near real-time ML pipelines using Kafka and Spark Streaming. Work with AWS and SageMaker MLOps ecosystem. Requirements 6+ years of experience in software engineering, data engineering, or MLOps roles. Strong programming expertise in Python, with hands-on experience in Pandas, PySpark, and PyArrow. Deep understanding of the Hadoop ecosystem, distributed computing, and performance tuning. Experience with CI/CD pipelines and best practices in ML environments. Hands-on experience with monitoring tools for ML pipeline health and performance. Strong collaboration skills with experience working in cross-functional teams (platform, data science, engineering). Experience contributing to or building internal MLOps frameworks/platforms. Familiarity with SLURM clusters or other distributed job schedulers. Exposure to Kafka, Spark Streaming, or other real-time data processing technologies. Understanding of ML lifecycle management, including versioning, deployment, and drift detection. - KB1 Ref: Pittsburgh
Job Description Job Description iSoftStone, Inc. is seeking an IT Operations Engineer (L2) - Warehouse Operations to join our Team in Whitestown, Indiana! This is a one-year contract position which requires the employee to work 100% onsite! Candidates MUST be fluent in Chinese language! We are seeking an experienced IT Operations Engineer (L2) to support warehouse and office IT operations. This role is responsible for advanced technical troubleshooting, infrastructure support, network issue resolution, handheld device management, monitoring response, and operational optimization in a 24/7 warehouse environment. The engineer will serve as the escalation point for complex IT issues and work closely with warehouse operations, vendors, and regional IT teams to ensure stable and efficient IT services. Responsibilities: -Provide advanced support for desktops, laptops, operating systems, office software, and warehouse applications. -Troubleshoot hardware, software, system performance, and operating system issues. -Support warehouse network connectivity, IP/DNS configuration, cabling, and advanced troubleshooting using tools such as ping, tracert, nslookup, telnet, and Wireshark. -Support printers, label printing systems, barcode scanners, PDA devices, and handheld terminals. -Perform system imaging, patching, firmware upgrades, backup, recovery, and device reimaging. -Support conference room and video meeting equipment. -Participate in monitoring, incident response, escalation handling, and operational support for warehouse IT infrastructure. -Support IT asset management, SOP creation, documentation, and operational process improvement. -Coordinate with vendors and regional/global IT teams on issue resolution and infrastructure support. Qualifications: -Five+ years of IT support or IT operations experience. -Experience in e-commerce with warehouse, logistics, manufacturing, or retail environments. -Strong knowledge of Windows OS, computer hardware, printers, handheld devices, and networking. -Familiar with TCP/IP, DNS, DHCP, VLAN, Wi-Fi, and troubleshooting tools. -Experience with ticketing systems and IT service management processes. -Strong troubleshooting, communication, and documentation skills. -Ability to support shift-based or after-hours operations when required. Preferred Qualifications: -Experience with Zebra/Honeywell devices, Intune/MDM, or warehouse automation systems. -Experience with IT infrastructure. -Experience in management. Primary Location Rate: $22 - $55 per hour. iSoftStone is a global IT service and consulting company that creates value and drives success through technology solutions, service excellence, and digital innovation. We specialize in web and application development, software testing and support, data and content management, digital experience, accessibility, and data for machine learning and AI. With 20 delivery centers and more than 90,000 employees worldwide, iSoftStone is proud to serve some of the world's most well-known businesses, including 90+ Fortune Global 500 companies. Visit us at . iSoftStone is committed to the practice of equal opportunity for all its employees and applicants in employment, and does not discriminate on the basis of race or ethnicity, color, age, national origin, religion, creed, marital status, sex, pregnancy, gender, gender identity, sexual orientation, status as an honorably discharged veteran or disabled veteran or military status, political affiliation or belief, citizenship/status as a lawfully admitted immigrant authorized to work in the United States, or presence of any physical, sensory, or mental disability. In addition, reasonable accommodation will be made for known physical or mental limitations for all otherwise qualified persons with disabilities.
09/30/2026
Full time
Job Description Job Description iSoftStone, Inc. is seeking an IT Operations Engineer (L2) - Warehouse Operations to join our Team in Whitestown, Indiana! This is a one-year contract position which requires the employee to work 100% onsite! Candidates MUST be fluent in Chinese language! We are seeking an experienced IT Operations Engineer (L2) to support warehouse and office IT operations. This role is responsible for advanced technical troubleshooting, infrastructure support, network issue resolution, handheld device management, monitoring response, and operational optimization in a 24/7 warehouse environment. The engineer will serve as the escalation point for complex IT issues and work closely with warehouse operations, vendors, and regional IT teams to ensure stable and efficient IT services. Responsibilities: -Provide advanced support for desktops, laptops, operating systems, office software, and warehouse applications. -Troubleshoot hardware, software, system performance, and operating system issues. -Support warehouse network connectivity, IP/DNS configuration, cabling, and advanced troubleshooting using tools such as ping, tracert, nslookup, telnet, and Wireshark. -Support printers, label printing systems, barcode scanners, PDA devices, and handheld terminals. -Perform system imaging, patching, firmware upgrades, backup, recovery, and device reimaging. -Support conference room and video meeting equipment. -Participate in monitoring, incident response, escalation handling, and operational support for warehouse IT infrastructure. -Support IT asset management, SOP creation, documentation, and operational process improvement. -Coordinate with vendors and regional/global IT teams on issue resolution and infrastructure support. Qualifications: -Five+ years of IT support or IT operations experience. -Experience in e-commerce with warehouse, logistics, manufacturing, or retail environments. -Strong knowledge of Windows OS, computer hardware, printers, handheld devices, and networking. -Familiar with TCP/IP, DNS, DHCP, VLAN, Wi-Fi, and troubleshooting tools. -Experience with ticketing systems and IT service management processes. -Strong troubleshooting, communication, and documentation skills. -Ability to support shift-based or after-hours operations when required. Preferred Qualifications: -Experience with Zebra/Honeywell devices, Intune/MDM, or warehouse automation systems. -Experience with IT infrastructure. -Experience in management. Primary Location Rate: $22 - $55 per hour. iSoftStone is a global IT service and consulting company that creates value and drives success through technology solutions, service excellence, and digital innovation. We specialize in web and application development, software testing and support, data and content management, digital experience, accessibility, and data for machine learning and AI. With 20 delivery centers and more than 90,000 employees worldwide, iSoftStone is proud to serve some of the world's most well-known businesses, including 90+ Fortune Global 500 companies. Visit us at . iSoftStone is committed to the practice of equal opportunity for all its employees and applicants in employment, and does not discriminate on the basis of race or ethnicity, color, age, national origin, religion, creed, marital status, sex, pregnancy, gender, gender identity, sexual orientation, status as an honorably discharged veteran or disabled veteran or military status, political affiliation or belief, citizenship/status as a lawfully admitted immigrant authorized to work in the United States, or presence of any physical, sensory, or mental disability. In addition, reasonable accommodation will be made for known physical or mental limitations for all otherwise qualified persons with disabilities.
Senior Staff Machine Learning Engineer Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala, Java, and GoLang Leverage compute technologies such as Dask and RAPIDS Evangelize best practices in all aspects of the engineering and modeling lifecycles Help recruit, nurture, and retain top engineering talent Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 10 years of experience programming with Python, Java, Golang, or C++ At least 8 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 8 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 8 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 5+ years of experience architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference. 7+ years of experience optimizing ML algorithms, configurations, and infrastructure 7+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 9+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. Experience shaping long term cross-organizational machine learning strategy Ability to communicate complex technical concepts clearly to executive leadership Recognized leader in the ML industry through conference presentations, papers, blog posts, open source contributions or patents Experience developing high-performing ML engineers with an inspiring leadership style Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $314,800 - $359,300 for Sr. Staff Machine Learning Engineer Plano, TX: $286,200 - $326,700 for Sr. Staff Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
09/30/2026
Full time
Senior Staff Machine Learning Engineer Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala, Java, and GoLang Leverage compute technologies such as Dask and RAPIDS Evangelize best practices in all aspects of the engineering and modeling lifecycles Help recruit, nurture, and retain top engineering talent Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 10 years of experience programming with Python, Java, Golang, or C++ At least 8 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 8 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 8 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 5+ years of experience architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference. 7+ years of experience optimizing ML algorithms, configurations, and infrastructure 7+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 9+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. Experience shaping long term cross-organizational machine learning strategy Ability to communicate complex technical concepts clearly to executive leadership Recognized leader in the ML industry through conference presentations, papers, blog posts, open source contributions or patents Experience developing high-performing ML engineers with an inspiring leadership style Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $314,800 - $359,300 for Sr. Staff Machine Learning Engineer Plano, TX: $286,200 - $326,700 for Sr. Staff Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Machine Learning Engineer 4 (IC) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Machine Learning Engineer 4 McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 San Jose, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
09/30/2026
Full time
Machine Learning Engineer 4 (IC) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Machine Learning Engineer 4 McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 San Jose, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Staff Machine Learning Engineer Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala, Java, and GoLang Leverage compute technologies such as Dask and RAPIDS Evangelize best practices in all aspects of the engineering and modeling lifecycles Help recruit, nurture, and retain top engineering talent Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 8 years of experience programming with Python, Java, Golang, or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 5 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference. 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 7+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. Ability to communicate complex technical concepts clearly to a variety of audiences Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions or patents Experience developing high-performing ML engineers with an inspiring leadership style Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $269,100 - $307,200 for Staff Machine Learning Engineer Plano, TX: $244,700 - $279,200 for Staff Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
09/30/2026
Full time
Staff Machine Learning Engineer Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala, Java, and GoLang Leverage compute technologies such as Dask and RAPIDS Evangelize best practices in all aspects of the engineering and modeling lifecycles Help recruit, nurture, and retain top engineering talent Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 8 years of experience programming with Python, Java, Golang, or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 5 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference. 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 7+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. Ability to communicate complex technical concepts clearly to a variety of audiences Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions or patents Experience developing high-performing ML engineers with an inspiring leadership style Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $269,100 - $307,200 for Staff Machine Learning Engineer Plano, TX: $244,700 - $279,200 for Staff Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Staff Machine Learning Engineer Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala, Java, and GoLang Leverage compute technologies such as Dask and RAPIDS Evangelize best practices in all aspects of the engineering and modeling lifecycles Help recruit, nurture, and retain top engineering talent Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 8 years of experience programming with Python, Java, Golang, or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 5 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference. 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 7+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. Ability to communicate complex technical concepts clearly to a variety of audiences Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions or patents Experience developing high-performing ML engineers with an inspiring leadership style Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $269,100 - $307,200 for Staff Machine Learning Engineer Plano, TX: $244,700 - $279,200 for Staff Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
09/30/2026
Full time
Staff Machine Learning Engineer Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala, Java, and GoLang Leverage compute technologies such as Dask and RAPIDS Evangelize best practices in all aspects of the engineering and modeling lifecycles Help recruit, nurture, and retain top engineering talent Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 8 years of experience programming with Python, Java, Golang, or C++ At least 6 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 6 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 5 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 3+ years of experience architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference. 5+ years of experience optimizing ML algorithms, configurations, and infrastructure 5+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 7+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. Ability to communicate complex technical concepts clearly to a variety of audiences Driving impacts in the ML industry through conference presentations, papers, blog posts, open source contributions or patents Experience developing high-performing ML engineers with an inspiring leadership style Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $269,100 - $307,200 for Staff Machine Learning Engineer Plano, TX: $244,700 - $279,200 for Staff Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Machine Learning Engineer 4 (IC) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Machine Learning Engineer 4 McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 San Jose, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
09/30/2026
Full time
Machine Learning Engineer 4 (IC) Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following: Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation) Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications Retrain, maintain, and monitor models in production Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale Construct optimized data pipelines to feed ML models Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI Use programming languages like Python, Scala, or Java Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 4 years of experience programming with Python, Java, Golang, or C++ At least 4 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 4 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI/ML data At least 2 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized ML software systems Preferred Qualifications: Master's or doctoral degree in computer science, electrical engineering, mathematics, or related field 3+ years of experience optimizing ML algorithms, configurations, and infrastructure 3+ years of experience following software development best practices including source control, testing, code reviews, CI/CD, etc. 3+ years of experience building resilient software solutions with pre-production testing, advanced deployment techniques (one-box, blue/green, gradual dial-up), monitoring, alarms, and preparing incident response plans. 3+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 3+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. 1+ years of experience as a technical lead developing ML solutions using industry best practices, patterns, and automation Authored/co-authored a paper on a ML technique, model, or proof of concept At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (e.g. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-3, and O-1, or any other forms of work authorization that require immigration support from an employer ). The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. Cambridge, MA: $197,300 - $225,100 for Machine Learning Engineer 4 McLean, VA: $197,300 - $225,100 for Machine Learning Engineer 4 New York, NY: $215,200 - $245,600 for Machine Learning Engineer 4 San Francisco, CA: $215,200 - $245,600 for Machine Learning Engineer 4 San Jose, CA: $215,200 - $245,600 for Machine Learning Engineer 4 Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
Senior Staff Machine Learning Engineer Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala, Java, and GoLang Leverage compute technologies such as Dask and RAPIDS Evangelize best practices in all aspects of the engineering and modeling lifecycles Help recruit, nurture, and retain top engineering talent Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 10 years of experience programming with Python, Java, Golang, or C++ At least 8 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 8 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 8 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 5+ years of experience architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference. 7+ years of experience optimizing ML algorithms, configurations, and infrastructure 7+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 9+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. Experience shaping long term cross-organizational machine learning strategy Ability to communicate complex technical concepts clearly to executive leadership Recognized leader in the ML industry through conference presentations, papers, blog posts, open source contributions or patents Experience developing high-performing ML engineers with an inspiring leadership style Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $314,800 - $359,300 for Sr. Staff Machine Learning Engineer Plano, TX: $286,200 - $326,700 for Sr. Staff Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).
09/30/2026
Full time
Senior Staff Machine Learning Engineer Do you love building and pioneering in the AI and technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative delivery environment? At Capital One, you'll be part of a big group of makers, breakers, doers and disruptors who love to solve real problems and meet real customer needs. We are seeking Machine Learning Engineers who are passionate about leveraging cutting-edge open source frameworks, advanced algorithms, and emerging technologies to join our team. As a Machine Learning Engineer, you'll have the opportunity to be on the forefront of driving major AI transformations and scaling production models across Capital One. What You'll Do: Deliver ML models and software components that solve challenging business problems in the financial services industry, working in collaboration with the Product, Architecture, Engineering, and Data Science teams Drive the creation and evolution of ML models and software that enable state-of-the-art intelligent systems Lead large-scale ML initiatives with the customer in mind Leverage cloud-based architectures and technologies to deliver optimized ML models at scale Optimize data pipelines to feed ML models Use programming languages like Python, Scala, Java, and GoLang Leverage compute technologies such as Dask and RAPIDS Evangelize best practices in all aspects of the engineering and modeling lifecycles Help recruit, nurture, and retain top engineering talent Basic Qualifications: Bachelor's Degree or higher in Computer Science, Machine Learning or a related quantitative field (Statistics, Economics, Operations Research, Analytics, Mathematics, Engineering) At least 10 years of experience programming with Python, Java, Golang, or C++ At least 8 years of Machine Learning experience using industry standard frameworks PyTorch or Tensorflow and libraries (Pandas, NumPy, Scikit-learn) At least 8 years of experience using and operating large scale distributed systems (Spark, Ray) to prepare AI or Machine Learning data At least 8 years of experience deploying and operating Machine Learning solutions in production and operating production services in the cloud (AWS, GCP, Azure) and using Kubernetes to manage large scale containerized Machine Learning software systems Preferred Qualifications: Master's or Doctoral Degree in Computer Science, Electrical Engineering, Mathematics, or related field 5+ years of experience architecting and designing resilient, large-scale, production machine learning systems from data preparation, to model training, and inference. 7+ years of experience optimizing ML algorithms, configurations, and infrastructure 7+ years of experience working with Machine Learning techniques (Supervised, semi-supervised, and unsupervised, reinforcement learning, etc.) model types (Regression, Classification, Clustering, etc.), model Architectures (RNNs, CNNs, LSTMs, Transformers), training concepts (loss function, hyperparameters, regularization), and how to evaluate model accuracy and diagnose and address common issues (underfitting, overfitting) 9+ years of experience designing, implementing, and scaling production-ready data pipelines for training and evaluating ML models. Experience shaping long term cross-organizational machine learning strategy Ability to communicate complex technical concepts clearly to executive leadership Recognized leader in the ML industry through conference presentations, papers, blog posts, open source contributions or patents Experience developing high-performing ML engineers with an inspiring leadership style Capital One will consider sponsoring a new qualified applicant for employment authorization for this position. The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked. McLean, VA: $314,800 - $359,300 for Sr. Staff Machine Learning Engineer Plano, TX: $286,200 - $326,700 for Sr. Staff Machine Learning Engineer Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter. This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan. Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website . Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level. This role is expected to accept applications for a minimum of 5 business days.No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections ; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1- or via email at . All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).