Credit Acceptance Corporation
San Francisco, California
At Credit Acceptance, we are building on the foundation of a successful business while investing in the future through technology, data, AI, and continuous improvement. As we continue to evolve, our Team Members play an active role in improving experiences, driving meaningful change, and shaping the future of our business. We foster an environment where people are empowered to contribute ideas, develop their careers, make an impact, and help lead our continued transformation. Our Engineering teams are helping shape the future of Credit Acceptance through innovation. Team Members collaborate across the organization to solve complex challenges, build impactful solutions, challenge the status quo, and support informed business decisions. We value curiosity, continuous learning, and new ideas from people who are passionate about driving modernization and meaningful business impact. Credit Acceptance makes vehicle ownership possible for consumers other lenders decline, through a dealer network, an originations and servicing platform, and pricing and decisioning models refined over decades. The technology estate underneath this business was built project by project over a long period, and modernizing it is the largest engineering opportunity at the company. As the Distinguished Engineer, you are the senior-most individual contributor in Technology, reporting directly to the Chief Technology Officer. You will set the multi-year technical strategy for the company while staying close enough to the code and systems to remain credible - this is not an architect role that reviews designs from a distance, nor a strong engineer role that goes deep in a single domain. Two things sit at the center of the job: the target architecture for the enterprise, and the architecture for running AI agents across it. You will not own a team; you will own the hardest technical problems the company has, and get them resolved by convincing the people who do own the teams. Outcomes and Activities Enterprise target architecture: Convene the principal and staff engineers who own each domain to define the end-state architecture across originations, servicing, marketplace, data, and AI, and use it to drive the multi-year plan. Ensure every component has a defined interface with stated inputs and outputs, every unmade decision has a written principle standing in for it, and the document states honestly how close the organization is to the end state. Publish the architecture, including the tradeoffs that were rejected and why. Agentic AI architecture across the enterprise: Own the enterprise agent architecture spanning servicing and collections, the dealer side, originations, and internal engineering and back-office workflows, built on one common foundation rather than one stack per team. Establish a governed way for agents to discover and call business capabilities, data, and tools, using the Model Context Protocol as the integration layer. Define an identity and authorization model that treats an agent as a first-class actor with its own least-privilege entitlements, along with observability, evaluation, and cost controls that work consistently for every agent. Set policy on what an agent may decide independently versus what stays with a person, and define where that line moves and on what evidence. Design the architecture to produce an examiner-ready record of what each agent saw, recommended, and touched, and set the evaluation and adversarial testing approach that makes agent outcomes defensible. Ensure the same foundation and controls apply to agents built by outside firms on the company's behalf. Migration off on-premises infrastructure: Own the technical strategy for migrating transactional and analytical workloads off on-premises infrastructure to the company's strategic targets - AWS for transactional workloads and Databricks for analytical workloads - determining what lifts and shifts, what replatforms, what gets re-architected on the way versus after, and what the steady-state cost looks like. Own the resilience and recovery architecture for the target state. Data platform architecture: Set the architecture for change-data-capture-based ingestion, streaming where warranted, and a modern warehouse with no job-scheduling dependency, moving the organization from largely batch analytical data movement to real time. Help settle the open question of who owns the transactional and analytical data models. Real-time decisioning, model serving, and experimentation: Architect for pricing and credit model and scorecard updates that drop in as a layer rather than requiring a multi-quarter rebuild, and for an experimentation platform that supports orders of magnitude more concurrent experiments than a conventional release process allows. Marketplace and search architecture: Own the architecture and interfaces for a unified vehicle inventory, consumer search, and lead routing platform, ensuring the data model supports more than one class of seller from the start. AI-native engineering practice: Define the golden path for how software is specified, generated, reviewed, tested, and deployed, including the analyzers and gates that enforce it, and measure the inputs to that practice rather than just the outputs. Hold the build-versus-buy bar for AI capability, building what is core competency and about to scale, and renting the rest. Performance and peak readiness: Own the instrumentation, baseline, and remediation architecture needed to meet the standard that no page takes more than one second. Own peak-event headroom, including understanding the limits of each system and maintaining tested degradation levers ahead of need. Operating approach: Move the organization from project-shaped systems toward owned platforms with stated contracts. Document every component's inputs and outputs, and be ready to argue why closely coupled components should not be consolidated into one. Work toward a two-week requirement-to-production iteration bar, treating architecture that cannot be delivered incrementally as unfinished. Name what comes off the plan whenever a change in direction is requested. Make the call and own the outcome on architectural recommendations. Treat security, privacy, auditability, and regulatory obligation as design inputs from the start in this regulated environment. Hands-on, not hands-off: This is not optional. Stay personally engaged in the technical work rather than directing it from a distance. Join design reviews, write proofs of concept, read code, and engage in incident analysis. This hands-on time does not replace the work the teams own, and it is not a fallback when delivery slips. Every engineer in the organization will read how you write, how you argue a design, how you handle being wrong, and how you treat engineers who disagree with you as the bar. Staff and principal engineers should be measurably better at this work for having worked with you. Success measures: A published target architecture the domain owners agree with and that the annual and long-range plans are actually written against; an enterprise agent foundation that internal teams and partners build on, with access controls, evaluation, and audit records in place; a credible, dated, costed migration path off on-premises infrastructure and demonstrated progress against it; measured improvement in chosen input metrics such as latency, availability, recovery objectives, deployment frequency, and experiment throughput; growth in the number of principal and staff engineers who are better at this work as a result of this role; and architectural decisions that stop being relitigated because the reasoning was written down and the interfaces were clear. Competencies Customer Empathy: Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer's shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer-centric experience. Engineering Excellence: Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves the pursuit and achievement of high standards, best practices, innovation, and superior solutions. One Team: A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective purpose. This enables teams to navigate challenges and pursue shared objectives more effectively. Owner's Mindset: Owner's Mindset involves adopting a set of behaviors that reflect a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain. As an owner, you understand the business and your domain(s) deeply and solve for the right outcome for the domain(s) and the business. Requirements Bachelor's degree in Computer Science, Information Systems, or closely related field of study; or equivalent work experience Minimum 10 years of software engineering experience or comparable depth of experience A track record of setting and delivering multi-year technical strategy at enterprise scale, as an individual contributor rather than through a reporting line. Depth in distributed systems architecture, and in at least two of: agentic or LLM-based systems in production; large-scale cloud migration of transactional systems; modern data platform and lakehouse architecture; machine learning platform and model serving; high-traffic consumer or marketplace applications. Architecture ownership of agentic or LLM-based systems that reached production, including how tool and data access were governed, how the system was evaluated . click apply for full job details
11/14/2026
Full time
At Credit Acceptance, we are building on the foundation of a successful business while investing in the future through technology, data, AI, and continuous improvement. As we continue to evolve, our Team Members play an active role in improving experiences, driving meaningful change, and shaping the future of our business. We foster an environment where people are empowered to contribute ideas, develop their careers, make an impact, and help lead our continued transformation. Our Engineering teams are helping shape the future of Credit Acceptance through innovation. Team Members collaborate across the organization to solve complex challenges, build impactful solutions, challenge the status quo, and support informed business decisions. We value curiosity, continuous learning, and new ideas from people who are passionate about driving modernization and meaningful business impact. Credit Acceptance makes vehicle ownership possible for consumers other lenders decline, through a dealer network, an originations and servicing platform, and pricing and decisioning models refined over decades. The technology estate underneath this business was built project by project over a long period, and modernizing it is the largest engineering opportunity at the company. As the Distinguished Engineer, you are the senior-most individual contributor in Technology, reporting directly to the Chief Technology Officer. You will set the multi-year technical strategy for the company while staying close enough to the code and systems to remain credible - this is not an architect role that reviews designs from a distance, nor a strong engineer role that goes deep in a single domain. Two things sit at the center of the job: the target architecture for the enterprise, and the architecture for running AI agents across it. You will not own a team; you will own the hardest technical problems the company has, and get them resolved by convincing the people who do own the teams. Outcomes and Activities Enterprise target architecture: Convene the principal and staff engineers who own each domain to define the end-state architecture across originations, servicing, marketplace, data, and AI, and use it to drive the multi-year plan. Ensure every component has a defined interface with stated inputs and outputs, every unmade decision has a written principle standing in for it, and the document states honestly how close the organization is to the end state. Publish the architecture, including the tradeoffs that were rejected and why. Agentic AI architecture across the enterprise: Own the enterprise agent architecture spanning servicing and collections, the dealer side, originations, and internal engineering and back-office workflows, built on one common foundation rather than one stack per team. Establish a governed way for agents to discover and call business capabilities, data, and tools, using the Model Context Protocol as the integration layer. Define an identity and authorization model that treats an agent as a first-class actor with its own least-privilege entitlements, along with observability, evaluation, and cost controls that work consistently for every agent. Set policy on what an agent may decide independently versus what stays with a person, and define where that line moves and on what evidence. Design the architecture to produce an examiner-ready record of what each agent saw, recommended, and touched, and set the evaluation and adversarial testing approach that makes agent outcomes defensible. Ensure the same foundation and controls apply to agents built by outside firms on the company's behalf. Migration off on-premises infrastructure: Own the technical strategy for migrating transactional and analytical workloads off on-premises infrastructure to the company's strategic targets - AWS for transactional workloads and Databricks for analytical workloads - determining what lifts and shifts, what replatforms, what gets re-architected on the way versus after, and what the steady-state cost looks like. Own the resilience and recovery architecture for the target state. Data platform architecture: Set the architecture for change-data-capture-based ingestion, streaming where warranted, and a modern warehouse with no job-scheduling dependency, moving the organization from largely batch analytical data movement to real time. Help settle the open question of who owns the transactional and analytical data models. Real-time decisioning, model serving, and experimentation: Architect for pricing and credit model and scorecard updates that drop in as a layer rather than requiring a multi-quarter rebuild, and for an experimentation platform that supports orders of magnitude more concurrent experiments than a conventional release process allows. Marketplace and search architecture: Own the architecture and interfaces for a unified vehicle inventory, consumer search, and lead routing platform, ensuring the data model supports more than one class of seller from the start. AI-native engineering practice: Define the golden path for how software is specified, generated, reviewed, tested, and deployed, including the analyzers and gates that enforce it, and measure the inputs to that practice rather than just the outputs. Hold the build-versus-buy bar for AI capability, building what is core competency and about to scale, and renting the rest. Performance and peak readiness: Own the instrumentation, baseline, and remediation architecture needed to meet the standard that no page takes more than one second. Own peak-event headroom, including understanding the limits of each system and maintaining tested degradation levers ahead of need. Operating approach: Move the organization from project-shaped systems toward owned platforms with stated contracts. Document every component's inputs and outputs, and be ready to argue why closely coupled components should not be consolidated into one. Work toward a two-week requirement-to-production iteration bar, treating architecture that cannot be delivered incrementally as unfinished. Name what comes off the plan whenever a change in direction is requested. Make the call and own the outcome on architectural recommendations. Treat security, privacy, auditability, and regulatory obligation as design inputs from the start in this regulated environment. Hands-on, not hands-off: This is not optional. Stay personally engaged in the technical work rather than directing it from a distance. Join design reviews, write proofs of concept, read code, and engage in incident analysis. This hands-on time does not replace the work the teams own, and it is not a fallback when delivery slips. Every engineer in the organization will read how you write, how you argue a design, how you handle being wrong, and how you treat engineers who disagree with you as the bar. Staff and principal engineers should be measurably better at this work for having worked with you. Success measures: A published target architecture the domain owners agree with and that the annual and long-range plans are actually written against; an enterprise agent foundation that internal teams and partners build on, with access controls, evaluation, and audit records in place; a credible, dated, costed migration path off on-premises infrastructure and demonstrated progress against it; measured improvement in chosen input metrics such as latency, availability, recovery objectives, deployment frequency, and experiment throughput; growth in the number of principal and staff engineers who are better at this work as a result of this role; and architectural decisions that stop being relitigated because the reasoning was written down and the interfaces were clear. Competencies Customer Empathy: Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer's shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer-centric experience. Engineering Excellence: Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves the pursuit and achievement of high standards, best practices, innovation, and superior solutions. One Team: A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective purpose. This enables teams to navigate challenges and pursue shared objectives more effectively. Owner's Mindset: Owner's Mindset involves adopting a set of behaviors that reflect a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain. As an owner, you understand the business and your domain(s) deeply and solve for the right outcome for the domain(s) and the business. Requirements Bachelor's degree in Computer Science, Information Systems, or closely related field of study; or equivalent work experience Minimum 10 years of software engineering experience or comparable depth of experience A track record of setting and delivering multi-year technical strategy at enterprise scale, as an individual contributor rather than through a reporting line. Depth in distributed systems architecture, and in at least two of: agentic or LLM-based systems in production; large-scale cloud migration of transactional systems; modern data platform and lakehouse architecture; machine learning platform and model serving; high-traffic consumer or marketplace applications. Architecture ownership of agentic or LLM-based systems that reached production, including how tool and data access were governed, how the system was evaluated . click apply for full job details
Pay Range: $60.00hr - $65.00hr Job Overview: The candidate should possess 5+ years of hands-on experience in data modeling, data architecture, or database design roles. Responsibilities and Requirements: Demonstrate expert proficiency with Erwin Data Modeler (version 9.x or higher preferred), including subject areas, model templates, DDL generation, forward/reverse engineering, model versioning, and complete compare functionality. Utilize advanced SQL skills and a deep understanding of Oracle (11g/12c/19c/21c), including SQL, PL/SQL, schema design, and performance tuning fundamentals as they relate to data model design. Build both normalized (3NF) transactional models and dimensional models (star/snowflake) for reporting and analytics. Demonstrate strong command of DDL, including creating and modifying tables, indexes, constraints, sequences, synonyms, and materialized views. Apply solid understanding of data modeling theory, including entity-relationship modeling, normalization/denormalization, cardinality, keys, and referential integrity. Apply a solid grasp of database internals, including indexing strategies, partitioning, clustering, and query execution plans. Utilize data modeling best practices, including normalization, denormalization, surrogate keys, and slowly changing dimensions (SCD). Produce and maintain data dictionaries, metadata, and data lineage documentation. Demonstrate familiarity with version control (e.g., Git) and model comparison/diff tools. Demonstrate excellent communication skills with the ability to clearly explain modeling decisions to both technical and non-technical audiences. Demonstrate self-motivation with an ability to multitask. Work effectively in a team environment. Preferred Qualifications: Experience in a regulated industry (utilities, financial services, insurance, healthcare) with associated data governance and compliance requirements. Familiarity with additional modeling or metadata tools (e.g., ER/Studio, PowerDesigner, Collibra, Alation). Exposure to cloud data platforms (e.g., Oracle Cloud, AWS, Azure, Snowflake, Databricks) and hybrid on-prem/cloud modeling scenarios. Working knowledge of ETL/ELT tools and data integration patterns. Experience supporting master data management (MDM) or data governance initiatives. Experience working with Microsoft SQL Server. Experience working with PostgreSQL. Data vault experience. Experience with Hadoop/MongoDB. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.
11/14/2026
Full time
Pay Range: $60.00hr - $65.00hr Job Overview: The candidate should possess 5+ years of hands-on experience in data modeling, data architecture, or database design roles. Responsibilities and Requirements: Demonstrate expert proficiency with Erwin Data Modeler (version 9.x or higher preferred), including subject areas, model templates, DDL generation, forward/reverse engineering, model versioning, and complete compare functionality. Utilize advanced SQL skills and a deep understanding of Oracle (11g/12c/19c/21c), including SQL, PL/SQL, schema design, and performance tuning fundamentals as they relate to data model design. Build both normalized (3NF) transactional models and dimensional models (star/snowflake) for reporting and analytics. Demonstrate strong command of DDL, including creating and modifying tables, indexes, constraints, sequences, synonyms, and materialized views. Apply solid understanding of data modeling theory, including entity-relationship modeling, normalization/denormalization, cardinality, keys, and referential integrity. Apply a solid grasp of database internals, including indexing strategies, partitioning, clustering, and query execution plans. Utilize data modeling best practices, including normalization, denormalization, surrogate keys, and slowly changing dimensions (SCD). Produce and maintain data dictionaries, metadata, and data lineage documentation. Demonstrate familiarity with version control (e.g., Git) and model comparison/diff tools. Demonstrate excellent communication skills with the ability to clearly explain modeling decisions to both technical and non-technical audiences. Demonstrate self-motivation with an ability to multitask. Work effectively in a team environment. Preferred Qualifications: Experience in a regulated industry (utilities, financial services, insurance, healthcare) with associated data governance and compliance requirements. Familiarity with additional modeling or metadata tools (e.g., ER/Studio, PowerDesigner, Collibra, Alation). Exposure to cloud data platforms (e.g., Oracle Cloud, AWS, Azure, Snowflake, Databricks) and hybrid on-prem/cloud modeling scenarios. Working knowledge of ETL/ELT tools and data integration patterns. Experience supporting master data management (MDM) or data governance initiatives. Experience working with Microsoft SQL Server. Experience working with PostgreSQL. Data vault experience. Experience with Hadoop/MongoDB. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.
Job Description Senior Software Engineer $200,000 - $300,000 Base Salary + Equity New York City In Office A high-growth, venture-backed software company is hiring a Senior Software Engineer, Money Movement to help build the financial infrastructure behind its platform. You'll work across payroll, banking, payments, accounting, and ledger systems , building products responsible for moving and accurately tracking real money. You'll join a highly technical engineering team building financial products from the ground up, with ownership over systems where correctness, reliability, and auditability are non-negotiable . This is an opportunity to tackle complex fintech problems while directly shaping the architecture and technical standards behind a rapidly growing platform. What You'll Be Doing Build a payroll product from the ground up , including earnings calculations, tax withholding, payment execution, and filing integrations Develop bill pay and accounts payable infrastructure that allows customers to pay suppliers directly through the platform Build embedded banking capabilities that give customers faster access to their funds Own and scale accounting integrations , including synchronization of invoices, bills, journal entries, and sales data Design double-entry ledgers supporting financial products such as deposits, loyalty, and stored value Build systems around idempotency, reconciliation, atomicity, concurrent transactions, and financial correctness Establish engineering standards for money movement that can be adopted across the broader organization Leverage modern AI coding agents while maintaining the guardrails required for production financial systems Highlights ? Build Money Movement Products from 0 1 - Help launch new payroll, banking, and bill pay products from the ground up ? Embedded Banking & Payments - Build infrastructure that moves real customer funds and powers critical financial workflows ? Ledgers & Financial Correctness - Design auditable double-entry ledger systems where being off by even a cent matters ? Build Payroll from Scratch - Own core payroll infrastructure spanning earnings, tax withholding, payments, and filing integrations ? Accounting Infrastructure - Build and scale integrations with platforms such as QuickBooks and other accounting systems ? Deep PostgreSQL Engineering - Design transactional schemas and concurrency-safe systems capable of supporting growing financial workloads ? AI-First Engineering Culture - Use modern AI coding agents as part of the everyday development workflow ? High Ownership & Impact - Join a small engineering team where your architectural decisions can become company-wide standards What They're Looking For 5+ years of experience building production backend systems Strong backend engineering experience, ideally with TypeScript / Node.js Deep understanding of transactional systems , including idempotency, atomicity, retries, concurrency, and failure handling Strong experience with PostgreSQL , including schema design, transactions, and performance at scale Experience building systems where financial correctness and data integrity are critical Track record of debugging complex production problems and solving the underlying root cause Ability to independently own complex technical projects from architecture through production Nice to Have Experience building payroll, banking, payments, ledger, or other financial systems Experience with accounting platforms such as QuickBooks, NetSuite, or Xero Experience with payment processors, ACH, reconciliation, or money movement infrastructure Familiarity with Next.js, tRPC, Prisma, Vercel, ElastiCache, RDS, or RDS Proxy Experience working with AI coding tools such as Devin, Claude Code, or Cursor Background in retail, POS, multi-location operations, or cash reconciliation Experience establishing technical or architectural standards across engineering teams Compensation & Benefits $200,000 - $300,000 base salary Meaningful equity package Fully company-covered medical and dental benefits 401(k) Catered meals High-impact role with significant technical ownership Opportunity to build foundational financial products from the ground up
11/14/2026
Full time
Job Description Senior Software Engineer $200,000 - $300,000 Base Salary + Equity New York City In Office A high-growth, venture-backed software company is hiring a Senior Software Engineer, Money Movement to help build the financial infrastructure behind its platform. You'll work across payroll, banking, payments, accounting, and ledger systems , building products responsible for moving and accurately tracking real money. You'll join a highly technical engineering team building financial products from the ground up, with ownership over systems where correctness, reliability, and auditability are non-negotiable . This is an opportunity to tackle complex fintech problems while directly shaping the architecture and technical standards behind a rapidly growing platform. What You'll Be Doing Build a payroll product from the ground up , including earnings calculations, tax withholding, payment execution, and filing integrations Develop bill pay and accounts payable infrastructure that allows customers to pay suppliers directly through the platform Build embedded banking capabilities that give customers faster access to their funds Own and scale accounting integrations , including synchronization of invoices, bills, journal entries, and sales data Design double-entry ledgers supporting financial products such as deposits, loyalty, and stored value Build systems around idempotency, reconciliation, atomicity, concurrent transactions, and financial correctness Establish engineering standards for money movement that can be adopted across the broader organization Leverage modern AI coding agents while maintaining the guardrails required for production financial systems Highlights ? Build Money Movement Products from 0 1 - Help launch new payroll, banking, and bill pay products from the ground up ? Embedded Banking & Payments - Build infrastructure that moves real customer funds and powers critical financial workflows ? Ledgers & Financial Correctness - Design auditable double-entry ledger systems where being off by even a cent matters ? Build Payroll from Scratch - Own core payroll infrastructure spanning earnings, tax withholding, payments, and filing integrations ? Accounting Infrastructure - Build and scale integrations with platforms such as QuickBooks and other accounting systems ? Deep PostgreSQL Engineering - Design transactional schemas and concurrency-safe systems capable of supporting growing financial workloads ? AI-First Engineering Culture - Use modern AI coding agents as part of the everyday development workflow ? High Ownership & Impact - Join a small engineering team where your architectural decisions can become company-wide standards What They're Looking For 5+ years of experience building production backend systems Strong backend engineering experience, ideally with TypeScript / Node.js Deep understanding of transactional systems , including idempotency, atomicity, retries, concurrency, and failure handling Strong experience with PostgreSQL , including schema design, transactions, and performance at scale Experience building systems where financial correctness and data integrity are critical Track record of debugging complex production problems and solving the underlying root cause Ability to independently own complex technical projects from architecture through production Nice to Have Experience building payroll, banking, payments, ledger, or other financial systems Experience with accounting platforms such as QuickBooks, NetSuite, or Xero Experience with payment processors, ACH, reconciliation, or money movement infrastructure Familiarity with Next.js, tRPC, Prisma, Vercel, ElastiCache, RDS, or RDS Proxy Experience working with AI coding tools such as Devin, Claude Code, or Cursor Background in retail, POS, multi-location operations, or cash reconciliation Experience establishing technical or architectural standards across engineering teams Compensation & Benefits $200,000 - $300,000 base salary Meaningful equity package Fully company-covered medical and dental benefits 401(k) Catered meals High-impact role with significant technical ownership Opportunity to build foundational financial products from the ground up
Pay Range: $60.00hr - $65.00hr Job Overview: The candidate should possess 5+ years of hands-on experience in data modeling, data architecture, or database design roles. Responsibilities and Requirements: Demonstrate expert proficiency with Erwin Data Modeler (version 9.x or higher preferred), including subject areas, model templates, DDL generation, forward/reverse engineering, model versioning, and complete compare functionality. Utilize advanced SQL skills and a deep understanding of Oracle (11g/12c/19c/21c), including SQL, PL/SQL, schema design, and performance tuning fundamentals as they relate to data model design. Build both normalized (3NF) transactional models and dimensional models (star/snowflake) for reporting and analytics. Demonstrate strong command of DDL, including creating and modifying tables, indexes, constraints, sequences, synonyms, and materialized views. Apply solid understanding of data modeling theory, including entity-relationship modeling, normalization/denormalization, cardinality, keys, and referential integrity. Apply a solid grasp of database internals, including indexing strategies, partitioning, clustering, and query execution plans. Utilize data modeling best practices, including normalization, denormalization, surrogate keys, and slowly changing dimensions (SCD). Produce and maintain data dictionaries, metadata, and data lineage documentation. Demonstrate familiarity with version control (e.g., Git) and model comparison/diff tools. Demonstrate excellent communication skills with the ability to clearly explain modeling decisions to both technical and non-technical audiences. Demonstrate self-motivation with an ability to multitask. Work effectively in a team environment. Preferred Qualifications: Experience in a regulated industry (utilities, financial services, insurance, healthcare) with associated data governance and compliance requirements. Familiarity with additional modeling or metadata tools (e.g., ER/Studio, PowerDesigner, Collibra, Alation). Exposure to cloud data platforms (e.g., Oracle Cloud, AWS, Azure, Snowflake, Databricks) and hybrid on-prem/cloud modeling scenarios. Working knowledge of ETL/ELT tools and data integration patterns. Experience supporting master data management (MDM) or data governance initiatives. Experience working with Microsoft SQL Server. Experience working with PostgreSQL. Data vault experience. Experience with Hadoop/MongoDB. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.
11/14/2026
Full time
Pay Range: $60.00hr - $65.00hr Job Overview: The candidate should possess 5+ years of hands-on experience in data modeling, data architecture, or database design roles. Responsibilities and Requirements: Demonstrate expert proficiency with Erwin Data Modeler (version 9.x or higher preferred), including subject areas, model templates, DDL generation, forward/reverse engineering, model versioning, and complete compare functionality. Utilize advanced SQL skills and a deep understanding of Oracle (11g/12c/19c/21c), including SQL, PL/SQL, schema design, and performance tuning fundamentals as they relate to data model design. Build both normalized (3NF) transactional models and dimensional models (star/snowflake) for reporting and analytics. Demonstrate strong command of DDL, including creating and modifying tables, indexes, constraints, sequences, synonyms, and materialized views. Apply solid understanding of data modeling theory, including entity-relationship modeling, normalization/denormalization, cardinality, keys, and referential integrity. Apply a solid grasp of database internals, including indexing strategies, partitioning, clustering, and query execution plans. Utilize data modeling best practices, including normalization, denormalization, surrogate keys, and slowly changing dimensions (SCD). Produce and maintain data dictionaries, metadata, and data lineage documentation. Demonstrate familiarity with version control (e.g., Git) and model comparison/diff tools. Demonstrate excellent communication skills with the ability to clearly explain modeling decisions to both technical and non-technical audiences. Demonstrate self-motivation with an ability to multitask. Work effectively in a team environment. Preferred Qualifications: Experience in a regulated industry (utilities, financial services, insurance, healthcare) with associated data governance and compliance requirements. Familiarity with additional modeling or metadata tools (e.g., ER/Studio, PowerDesigner, Collibra, Alation). Exposure to cloud data platforms (e.g., Oracle Cloud, AWS, Azure, Snowflake, Databricks) and hybrid on-prem/cloud modeling scenarios. Working knowledge of ETL/ELT tools and data integration patterns. Experience supporting master data management (MDM) or data governance initiatives. Experience working with Microsoft SQL Server. Experience working with PostgreSQL. Data vault experience. Experience with Hadoop/MongoDB. Benefits: Our Benefits Include: Medical, Dental, and Vision Insurance 401(k) Retirement Plan Health Savings Account (HSA) Disability Insurance (Short-Term and Long-Term) Life and AD&D Insurance Paid Sick Leave (where required by applicable state or local law) Supplemental Insurance Plans Identity Theft Protection Pet Insurance Employee Wellness Programs Employee Assistance Program (EAP) Career Growth and Professional Development Opportunities Disclaimer: Benefits eligibility, accrual rates, and usage limits may vary based on employment status, length of service, and work location. Paid Sick Leave is provided in strict accordance with applicable state and municipal mandates. Cynet Systems Inc. reserves the right to modify, amend, or terminate any benefit plans at any time in accordance with applicable laws. About Cynet Systems Founded in 2010 and headquartered in the Washington, DC metro area, Cynet Systems Inc. is a leading technology staffing and workforce solutions company serving Fortune 500 companies, government agencies, and enterprise organizations across the United States and Canada. We deliver agile, scalable talent solutions across IT, engineering, life sciences, clinical, and professional staffing, powered by a high-performing recruitment engine operating across North America and Asia. As a nationally and locally certified Minority Business Enterprise (MBE), Cynet Systems is committed to helping organizations build high-performing teams while empowering professionals to grow rewarding careers. Our organization is certified to ISO 9001, ISO 14001, ISO 27001, and SOC 2 Type II standards, reflecting our commitment to quality, security, operational excellence, and customer success.
Job Description Senior Software Engineer, Backend - Booking Platform & APIs New York, NY We are representing a client that is hiring a Senior Software Engineer to lead the technical work and be the primary hands-on builder for our API. In your first year, multiple distribution partners and an authorized AI agent should complete real bookings through the same versioned API. You will evolve a production Rails and Postgres booking system used by millions of guests. The hard work is keeping prices and inventory correct, preventing overselling, surviving retries and partial failures, and reconciling systems when they disagree. Your ownership and team You will own the API's day-to-day technical roadmap and design decisions. In this hands-on individual contributor role, you will write production code, lead focused designs, guide migrations, and operate what you build. You will partner with our Staff Engineer on foundational domain and architecture choices. You will be the primary builder, but you will not work alone. Other backend engineers will contribute and build supply, PMS, and POS connectors using the patterns you establish. Product, Partnerships, and hotel operations will work with you from requirements through production launch. This is one of our highest-impact technical bets. What you build will determine how quickly Our client can enter new booking channels and categories. This role works from our NYC headquarters four days per week. The base salary range is $190,000-$210,000, plus equity, based on experience. What you'll do Build and launch the external Client API. Define stable, versioned contracts for live pricing, availability, holds, booking creation, changes, and cancellations. Make booking operations safe under real failures. Design idempotency, recovery, webhooks, and reconciliation for retries, timeouts, duplicates, and partial success. Evolve the core booking domain. Create clear models and invariants for products, schedules, prices, inventory, holds, carts, and bookings. Make the platform extensible. Prove that a new category such as events or food and beverage can use the shared booking model instead of requiring a separate system. Own the architecture for supply, PMS, and POS integrations. Create reusable connector patterns so other engineers can add partners without reinventing failure handling and reconciliation. Define the data contracts that keep inventory discoverable through OpenSearch. Search is supporting technology for this role, not its primary ownership area. Operate what you build. Set reliability and latency targets, and add the tests, metrics, dashboards, alerts, tracing, and runbooks needed for a critical booking path. Turn partner needs into durable platform capabilities, explain tradeoffs clearly, and raise the engineering bar through practical reviews and mentoring. How we ship We move quickly by building quality from the start. A change is done when it is tested, deployed, observable, and safe to roll back. We use AI tools aggressively, but engineers remain accountable for architecture, correctness, and production outcomes. Tech stack Backend: Ruby on Rails, Node.js Data: Postgres, MongoDB Search: OpenSearch APIs and integrations: REST, OpenAPI, webhooks Infrastructure and observability: AWS, Datadog Testing: RSpec, Playwright Who you are We encourage candidates to apply even if they do not match every line below. You have 6+ years of software engineering experience, or a track record of equivalent impact. You have designed transactional systems, built external APIs or integration platforms, and safely evolved data models in production. You understand relational modeling, concurrency, consistency, asynchronous workflows, and distributed failures. You turn complex business rules into clear boundaries, lifecycle states, contracts, and invariants. You can lead an important system as its primary builder while collaborating on foundational decisions. You care about correctness, testing, observability, and safe rollouts. You communicate directly, listen well, and improve the work of engineers around you. Ruby on Rails experience is useful, but not required. Experience with marketplaces, reservations, inventory-constrained commerce, public APIs, or third-party booking systems is also helpful. We care most about your backend judgment, track record, and ability to learn. What success looks like in 6-12 months Multiple distribution partners and an authorized AI agent complete real bookings through one reusable API. The full booking lifecycle meets agreed targets for correctness, success rate, latency, overselling, recovery, and reconciliation. Reusable contracts and connector patterns materially reduce the work required to onboard the next partner. The shared booking model supports at least one new category without a separate booking system. As the API becomes a company-wide platform, this role can grow toward Staff through demonstrated impact across the booking domain, integrations, and distribution. Benefits Health, dental, and vision coverage, with our client contributing to monthly premiums Stock options 401(k) plan Unlimited paid time off Paid parental leave
11/14/2026
Full time
Job Description Senior Software Engineer, Backend - Booking Platform & APIs New York, NY We are representing a client that is hiring a Senior Software Engineer to lead the technical work and be the primary hands-on builder for our API. In your first year, multiple distribution partners and an authorized AI agent should complete real bookings through the same versioned API. You will evolve a production Rails and Postgres booking system used by millions of guests. The hard work is keeping prices and inventory correct, preventing overselling, surviving retries and partial failures, and reconciling systems when they disagree. Your ownership and team You will own the API's day-to-day technical roadmap and design decisions. In this hands-on individual contributor role, you will write production code, lead focused designs, guide migrations, and operate what you build. You will partner with our Staff Engineer on foundational domain and architecture choices. You will be the primary builder, but you will not work alone. Other backend engineers will contribute and build supply, PMS, and POS connectors using the patterns you establish. Product, Partnerships, and hotel operations will work with you from requirements through production launch. This is one of our highest-impact technical bets. What you build will determine how quickly Our client can enter new booking channels and categories. This role works from our NYC headquarters four days per week. The base salary range is $190,000-$210,000, plus equity, based on experience. What you'll do Build and launch the external Client API. Define stable, versioned contracts for live pricing, availability, holds, booking creation, changes, and cancellations. Make booking operations safe under real failures. Design idempotency, recovery, webhooks, and reconciliation for retries, timeouts, duplicates, and partial success. Evolve the core booking domain. Create clear models and invariants for products, schedules, prices, inventory, holds, carts, and bookings. Make the platform extensible. Prove that a new category such as events or food and beverage can use the shared booking model instead of requiring a separate system. Own the architecture for supply, PMS, and POS integrations. Create reusable connector patterns so other engineers can add partners without reinventing failure handling and reconciliation. Define the data contracts that keep inventory discoverable through OpenSearch. Search is supporting technology for this role, not its primary ownership area. Operate what you build. Set reliability and latency targets, and add the tests, metrics, dashboards, alerts, tracing, and runbooks needed for a critical booking path. Turn partner needs into durable platform capabilities, explain tradeoffs clearly, and raise the engineering bar through practical reviews and mentoring. How we ship We move quickly by building quality from the start. A change is done when it is tested, deployed, observable, and safe to roll back. We use AI tools aggressively, but engineers remain accountable for architecture, correctness, and production outcomes. Tech stack Backend: Ruby on Rails, Node.js Data: Postgres, MongoDB Search: OpenSearch APIs and integrations: REST, OpenAPI, webhooks Infrastructure and observability: AWS, Datadog Testing: RSpec, Playwright Who you are We encourage candidates to apply even if they do not match every line below. You have 6+ years of software engineering experience, or a track record of equivalent impact. You have designed transactional systems, built external APIs or integration platforms, and safely evolved data models in production. You understand relational modeling, concurrency, consistency, asynchronous workflows, and distributed failures. You turn complex business rules into clear boundaries, lifecycle states, contracts, and invariants. You can lead an important system as its primary builder while collaborating on foundational decisions. You care about correctness, testing, observability, and safe rollouts. You communicate directly, listen well, and improve the work of engineers around you. Ruby on Rails experience is useful, but not required. Experience with marketplaces, reservations, inventory-constrained commerce, public APIs, or third-party booking systems is also helpful. We care most about your backend judgment, track record, and ability to learn. What success looks like in 6-12 months Multiple distribution partners and an authorized AI agent complete real bookings through one reusable API. The full booking lifecycle meets agreed targets for correctness, success rate, latency, overselling, recovery, and reconciliation. Reusable contracts and connector patterns materially reduce the work required to onboard the next partner. The shared booking model supports at least one new category without a separate booking system. As the API becomes a company-wide platform, this role can grow toward Staff through demonstrated impact across the booking domain, integrations, and distribution. Benefits Health, dental, and vision coverage, with our client contributing to monthly premiums Stock options 401(k) plan Unlimited paid time off Paid parental leave
Job Description Job Description: Data Scientist - Fraud Analysis Location: Austin, TX / Boston, MA / Chandler, AZ / Chicago, IL / Conshohocken, PA / Los Angeles, CA / Miami, FL / New York City, NY / Omaha / La Vista, NE / Plymouth Meeting, PA / San Francisco, CA / San Jose, CA / Scottsdale, AZ / Washington, D.C. / Wilmington, NC Job Type: Full-Time Roles & Responsibilities: As a member of the Fraud Analytics team, you will be responsible for identifying, investigating, and mitigating fraudulent activities using advanced data methodologies: Core Analytical & Comparison Tasks: Compare outputs of two systems or data sources that should behave the same, isolate the differences, and document root causes and thresholds. Strategy & ML Collaboration: Investigate suspicious transactions to determine root causes. Be comfortable working alongside an ML team and experienced in evaluating model performance. Fraud Assessment & System Development: Conduct in-depth assessments on evolving trends (including merchant fraud, first-party, and third-party fraud). Design and implement fraud rules within workflow systems and build comprehensive monitoring reports. Stakeholder Management: Generate high-impact reports and presentations for management, outlining risks, incidents, and effective mitigation strategies. Cross-Functional Collaboration: Partner with Risk, Operations, and Compliance teams to strengthen the overall fraud prevention ecosystem. Market Intelligence: Stay ahead of industry trends, regulatory shifts, and global best practices to continuously refine fraud prevention strategies. Technical & Professional Skills: Domain Expertise: Proven experience in Fraud Mitigation, First-Party Fraud, Transactional Fraud, Merchant Fraud, and Digital Fraud Detection. Technical Stack: Proficiency in Python and SQL on BigQuery and Jupyter notebook experience, then experience with rules management (reading, comparing, and translating rule logic). Analytical Rigour: Familiarity with fraud detection software, risk assessment methodologies, and regulatory compliance standards. Communication: Ability to distill complex data insights into clear, concise, and actionable presentations for leadership. Qualifications Education: Bachelor's degree in engineering, Technology, Computer Science, or a related quantitative field. Experience: 3+ years of dedicated data analytics experience specifically within fraud prevention or risk management. We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, citizenship status, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable law. verify.gov/sites/default/files/everify/posters/IER_RighttoWorkPoster.pdf
11/14/2026
Full time
Job Description Job Description: Data Scientist - Fraud Analysis Location: Austin, TX / Boston, MA / Chandler, AZ / Chicago, IL / Conshohocken, PA / Los Angeles, CA / Miami, FL / New York City, NY / Omaha / La Vista, NE / Plymouth Meeting, PA / San Francisco, CA / San Jose, CA / Scottsdale, AZ / Washington, D.C. / Wilmington, NC Job Type: Full-Time Roles & Responsibilities: As a member of the Fraud Analytics team, you will be responsible for identifying, investigating, and mitigating fraudulent activities using advanced data methodologies: Core Analytical & Comparison Tasks: Compare outputs of two systems or data sources that should behave the same, isolate the differences, and document root causes and thresholds. Strategy & ML Collaboration: Investigate suspicious transactions to determine root causes. Be comfortable working alongside an ML team and experienced in evaluating model performance. Fraud Assessment & System Development: Conduct in-depth assessments on evolving trends (including merchant fraud, first-party, and third-party fraud). Design and implement fraud rules within workflow systems and build comprehensive monitoring reports. Stakeholder Management: Generate high-impact reports and presentations for management, outlining risks, incidents, and effective mitigation strategies. Cross-Functional Collaboration: Partner with Risk, Operations, and Compliance teams to strengthen the overall fraud prevention ecosystem. Market Intelligence: Stay ahead of industry trends, regulatory shifts, and global best practices to continuously refine fraud prevention strategies. Technical & Professional Skills: Domain Expertise: Proven experience in Fraud Mitigation, First-Party Fraud, Transactional Fraud, Merchant Fraud, and Digital Fraud Detection. Technical Stack: Proficiency in Python and SQL on BigQuery and Jupyter notebook experience, then experience with rules management (reading, comparing, and translating rule logic). Analytical Rigour: Familiarity with fraud detection software, risk assessment methodologies, and regulatory compliance standards. Communication: Ability to distill complex data insights into clear, concise, and actionable presentations for leadership. Qualifications Education: Bachelor's degree in engineering, Technology, Computer Science, or a related quantitative field. Experience: 3+ years of dedicated data analytics experience specifically within fraud prevention or risk management. We are an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, citizenship status, age, disability, genetic information, protected veteran status, or any other characteristic protected by applicable law. verify.gov/sites/default/files/everify/posters/IER_RighttoWorkPoster.pdf
Job Description ABOUT US Shipwell is revolutionizing the logistics industry, redefining how the supply chain operates after centuries of convention. By uniting thousands of shippers and carriers on a cutting-edge technology platform, we are the leading software solution in logistics. With a bold startup energy and recent $35M Series B funding, Shipwell offers an exceptional opportunity to shape the future of a company destined for the Fortune 500. Supported by top-tier Silicon Valley and Austin investors, we are dedicated to driving innovation and growth in the supply chain sector. STAFF SOFTWARE ENGINEER The Staff Software Engineer role is a rare chance to shape the core systems of a high-growth startup that's breaking barriers in its third record-setting year. We're looking for a senior technical leader who does their best work hands-on: someone who takes on the hardest problems, writes code that sets the standard for others, and helps whole teams get better. Shipwell is an AI-forward engineering organization. We expect our Staff Engineers to be experienced with AI-driven development and to lead in it, using agents to multiply what they and their teams can deliver. As a Staff Engineer, you'll own complex, high-impact parts of our logistics platform from design through production. You'll work closely with engineering, product, and architecture to turn technical strategy into working software, and you'll mentor engineers along the way. WHAT YOU WILL DO WHEN YOU GET HERE • Own the design, build, and ongoing health of critical systems and services on the Shipwell platform. • Write high-quality, well-tested, production code, and spend most of your time building. • Use AI agents and multi-agent workflows day to day to speed up code generation, code review, and QA testing, while keeping quality high. • Build and share custom agents, establishing / reviewing projects AI Harness, and workflows that boost your own productivity and your team's. • Help set how Shipwell engineers use AI, including best practices, guardrails, and ways to measure its impact on speed and quality. • Break large, unclear problems into clear technical plans that teams can deliver step by step. • Lead technical efforts that span several teams, keeping work aligned and unblocking delivery. Acting as gate keeper and reviewer for project code patterns to support squad decisions and tuning, or critical shared entities • Work with architecture and product leadership to turn platform strategy into practical designs. • Raise the bar for code quality, testing, observability, and operations through code reviews and by example. • Find and fix performance bottlenecks, reliability risks, and tech debt before they become problems. • Mentor senior and mid-level engineers, including coaching them on working well with AI tools. • Take part in incident response and root-cause analysis, and drive lasting fixes. WHAT YOU NEED TO HAVE • BS/MS/Ph.D. in Computer Science or a related field, or equivalent practical experience. • 8+ years of professional software development, including 2+ years at a senior or staff level leading technical projects. • A track record of designing, building, and running distributed systems at scale in production. • Deep expertise in at least one modern language (e.g., Python / Django). • Strong knowledge of relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (e.g., MongoDB), including data modeling and query tuning. • Hands-on experience with cloud platforms (e.g., AWS, GCP, or Azure), containers, and CI/CD pipelines. • Experience with API design, event-driven architectures, and integrating with third-party systems. • Familiarity with agile development and DevOps practices, including owning what you ship in production with proven experience with GH actions or similar, for CI. • Excellent communication skills, with the ability to explain technical tradeoffs to engineers and non-technical partners. AI-Driven Development • Proven, hands-on experience using AI coding agents (e.g., Claude Code, Cursor, OpenAI Astra, or similar) to write production code. • Experience running multi-agent workflows, or "swarms," where several agents work in parallel on code generation, code review, and QA testing. • A track record of building your own agents, custom tools, or automated workflows to improve your productivity, such as review bots, test generators, or task runners. • Working knowledge of prompt and context engineering, tool/function calling, and protocols such as MCP (Model Context Protocol). • Sound judgment about where AI fits: you know when to trust agent output, how to check it, and how to keep AI-generated code secure, correct, and maintainable. • Portfolio or examples preferred: be ready to show agents, workflows, or projects you've built and how they changed the way you work. Nice to Have • Experience in logistics, supply chain, or transportation technology. • Background with EDI, carrier integrations, or high-volume transactional systems. WHY SHIPWELL? • Corporate headquarters in sunny Austin, TX. • An exciting growth opportunity at one of Austin's fastest-growing companies. • Comprehensive healthcare and dental insurance. • Engaging team-building events and fun office competitions. • Partial cell phone reimbursement. • An elite technology package, including a new MacBook Pro. • Competitive salary and career advancement opportunities. • A collaborative work environment where every voice is heard and valued. We are Shipwell, and we're not just about logistics. We're about innovation, transformation, and creating something extraordinary together. Shipwell is an equal opportunity employer, committed to fostering a diverse and inclusive workplace.
11/14/2026
Full time
Job Description ABOUT US Shipwell is revolutionizing the logistics industry, redefining how the supply chain operates after centuries of convention. By uniting thousands of shippers and carriers on a cutting-edge technology platform, we are the leading software solution in logistics. With a bold startup energy and recent $35M Series B funding, Shipwell offers an exceptional opportunity to shape the future of a company destined for the Fortune 500. Supported by top-tier Silicon Valley and Austin investors, we are dedicated to driving innovation and growth in the supply chain sector. STAFF SOFTWARE ENGINEER The Staff Software Engineer role is a rare chance to shape the core systems of a high-growth startup that's breaking barriers in its third record-setting year. We're looking for a senior technical leader who does their best work hands-on: someone who takes on the hardest problems, writes code that sets the standard for others, and helps whole teams get better. Shipwell is an AI-forward engineering organization. We expect our Staff Engineers to be experienced with AI-driven development and to lead in it, using agents to multiply what they and their teams can deliver. As a Staff Engineer, you'll own complex, high-impact parts of our logistics platform from design through production. You'll work closely with engineering, product, and architecture to turn technical strategy into working software, and you'll mentor engineers along the way. WHAT YOU WILL DO WHEN YOU GET HERE • Own the design, build, and ongoing health of critical systems and services on the Shipwell platform. • Write high-quality, well-tested, production code, and spend most of your time building. • Use AI agents and multi-agent workflows day to day to speed up code generation, code review, and QA testing, while keeping quality high. • Build and share custom agents, establishing / reviewing projects AI Harness, and workflows that boost your own productivity and your team's. • Help set how Shipwell engineers use AI, including best practices, guardrails, and ways to measure its impact on speed and quality. • Break large, unclear problems into clear technical plans that teams can deliver step by step. • Lead technical efforts that span several teams, keeping work aligned and unblocking delivery. Acting as gate keeper and reviewer for project code patterns to support squad decisions and tuning, or critical shared entities • Work with architecture and product leadership to turn platform strategy into practical designs. • Raise the bar for code quality, testing, observability, and operations through code reviews and by example. • Find and fix performance bottlenecks, reliability risks, and tech debt before they become problems. • Mentor senior and mid-level engineers, including coaching them on working well with AI tools. • Take part in incident response and root-cause analysis, and drive lasting fixes. WHAT YOU NEED TO HAVE • BS/MS/Ph.D. in Computer Science or a related field, or equivalent practical experience. • 8+ years of professional software development, including 2+ years at a senior or staff level leading technical projects. • A track record of designing, building, and running distributed systems at scale in production. • Deep expertise in at least one modern language (e.g., Python / Django). • Strong knowledge of relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (e.g., MongoDB), including data modeling and query tuning. • Hands-on experience with cloud platforms (e.g., AWS, GCP, or Azure), containers, and CI/CD pipelines. • Experience with API design, event-driven architectures, and integrating with third-party systems. • Familiarity with agile development and DevOps practices, including owning what you ship in production with proven experience with GH actions or similar, for CI. • Excellent communication skills, with the ability to explain technical tradeoffs to engineers and non-technical partners. AI-Driven Development • Proven, hands-on experience using AI coding agents (e.g., Claude Code, Cursor, OpenAI Astra, or similar) to write production code. • Experience running multi-agent workflows, or "swarms," where several agents work in parallel on code generation, code review, and QA testing. • A track record of building your own agents, custom tools, or automated workflows to improve your productivity, such as review bots, test generators, or task runners. • Working knowledge of prompt and context engineering, tool/function calling, and protocols such as MCP (Model Context Protocol). • Sound judgment about where AI fits: you know when to trust agent output, how to check it, and how to keep AI-generated code secure, correct, and maintainable. • Portfolio or examples preferred: be ready to show agents, workflows, or projects you've built and how they changed the way you work. Nice to Have • Experience in logistics, supply chain, or transportation technology. • Background with EDI, carrier integrations, or high-volume transactional systems. WHY SHIPWELL? • Corporate headquarters in sunny Austin, TX. • An exciting growth opportunity at one of Austin's fastest-growing companies. • Comprehensive healthcare and dental insurance. • Engaging team-building events and fun office competitions. • Partial cell phone reimbursement. • An elite technology package, including a new MacBook Pro. • Competitive salary and career advancement opportunities. • A collaborative work environment where every voice is heard and valued. We are Shipwell, and we're not just about logistics. We're about innovation, transformation, and creating something extraordinary together. Shipwell is an equal opportunity employer, committed to fostering a diverse and inclusive workplace.
Applied Researcher 5 At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, Capital One has been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to building world-class applied science and engineering teams and continue our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work touches every aspect of the research life cycle, from partnering with Academia to building production systems. We work with product, technology and business leaders to apply the state of the art in AI to our business. What You'll Do: Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact with their money. Leverage a broad stack of technologies; Pytorch, AWS Ultraclusters, Huggingface, Lightning, and more, to reveal the insights hidden within huge volumes of numeric and textual data. Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences. Flex your interpersonal skills to translate the complexity of your work into tangible business goals. Lead cross-functional research threads that bridge prototype model development and deployment with ML engineering partners, ensuring scientific insights translate to production impact The Ideal Candidate: You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it's about making the right decision for our customers Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You're not afraid to share a new idea A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You're passionate about talent development for your own team and beyond Technical. You're comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms Has a deep understanding of the foundations of AI methodologies Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF. An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes Experience in delivering libraries, platform level code or solution level code to existing products A professional with a track record of coming up with high quality ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects This role is designated as a hybrid position. You are expected to adhere to Capital One's hybrid policy regarding required on-site presence at your designated office location. Basic Qualifications: Currently has, or is in the process of obtaining, PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with an exception that required degree will be obtained on or before the scheduled start date plus 2 years of experience in Applied Research or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: PhD in Computer Science, Machine Learning, High Performance Computing, Applied Mathematics, Electrical Engineering or related fields LLM PhD focus on Generative AI (LLMs, Diffusion models and related approaches) or Masters with 5 years of industrial research experience in deep learning based techniques Multiple publications at top-tier venues (e.g., KDD, SIGIR, RecSys, NeurIPS, ICLR) on topics related to generative recommendation, retrieval-augmented generation (RAG), LLM-based agents, or large-scale semantic search Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens) Behavioral Models PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series) Multiple papers on topics relevant to training models on graph and sequential data structures at KDD, ICML, NeurIPs, ICLR Experience training foundation models from scratch or significantly adapting large-scale models (10B+ parameters) for complex financial data, transactional sequences, or user behavior graphs Track record of scaling graph models to greater than 50M nodes and operating within high-velocity, real-time streaming data environments Contributions to common open source frameworks (pytorch-geometric, DGL) Proposed new methods for inference or representation learning on graphs or sequences Worked datasets with 100m+ users Optimization (Training & Inference) Multiple years of experience or publications focused on model sparsification, quantization, training parallelism/partitioning design, or gradient checkpointing for 10B+ parameter models Hands-on experience with large-scale orchestration systems (Kubernetes, KubeRay) and contributions to common open-source ML frameworks (PyTorch-Geometric, DGL, HuggingFace) Experience building and deploying deep learning-based recommender systems at scale (100M+ users or items), ideally within travel, personalization, or e-commerce contexts Finetuning PhD focused on topics related to aligning Large Language Models for recommendation and search tasks via Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), or Direct Preference Optimization (DPO) Experience deploying a fine-tuned large language model optimized for serving performance Proven ability to design and lead small-scale research initiatives, guiding junior researchers or engineers through experimental design and evaluation 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. Cambridge, MA: $262,500 - $299,600 for Applied Researcher 5 McLean, VA: $262,500 - $299,600 for Applied Researcher 5 New York, NY: $286,400 - $326,800 for Applied Researcher 5 San Francisco, CA: $286,400 - $326,800 for Applied Researcher 5 San Jose, CA: $286,400 - $326,800 for Applied Researcher 5 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 . click apply for full job details
11/13/2026
Full time
Applied Researcher 5 At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, Capital One has been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to building world-class applied science and engineering teams and continue our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work touches every aspect of the research life cycle, from partnering with Academia to building production systems. We work with product, technology and business leaders to apply the state of the art in AI to our business. What You'll Do: Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact with their money. Leverage a broad stack of technologies; Pytorch, AWS Ultraclusters, Huggingface, Lightning, and more, to reveal the insights hidden within huge volumes of numeric and textual data. Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences. Flex your interpersonal skills to translate the complexity of your work into tangible business goals. Lead cross-functional research threads that bridge prototype model development and deployment with ML engineering partners, ensuring scientific insights translate to production impact The Ideal Candidate: You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it's about making the right decision for our customers Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You're not afraid to share a new idea A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You're passionate about talent development for your own team and beyond Technical. You're comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms Has a deep understanding of the foundations of AI methodologies Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF. An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes Experience in delivering libraries, platform level code or solution level code to existing products A professional with a track record of coming up with high quality ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects This role is designated as a hybrid position. You are expected to adhere to Capital One's hybrid policy regarding required on-site presence at your designated office location. Basic Qualifications: Currently has, or is in the process of obtaining, PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with an exception that required degree will be obtained on or before the scheduled start date plus 2 years of experience in Applied Research or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: PhD in Computer Science, Machine Learning, High Performance Computing, Applied Mathematics, Electrical Engineering or related fields LLM PhD focus on Generative AI (LLMs, Diffusion models and related approaches) or Masters with 5 years of industrial research experience in deep learning based techniques Multiple publications at top-tier venues (e.g., KDD, SIGIR, RecSys, NeurIPS, ICLR) on topics related to generative recommendation, retrieval-augmented generation (RAG), LLM-based agents, or large-scale semantic search Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens) Behavioral Models PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series) Multiple papers on topics relevant to training models on graph and sequential data structures at KDD, ICML, NeurIPs, ICLR Experience training foundation models from scratch or significantly adapting large-scale models (10B+ parameters) for complex financial data, transactional sequences, or user behavior graphs Track record of scaling graph models to greater than 50M nodes and operating within high-velocity, real-time streaming data environments Contributions to common open source frameworks (pytorch-geometric, DGL) Proposed new methods for inference or representation learning on graphs or sequences Worked datasets with 100m+ users Optimization (Training & Inference) Multiple years of experience or publications focused on model sparsification, quantization, training parallelism/partitioning design, or gradient checkpointing for 10B+ parameter models Hands-on experience with large-scale orchestration systems (Kubernetes, KubeRay) and contributions to common open-source ML frameworks (PyTorch-Geometric, DGL, HuggingFace) Experience building and deploying deep learning-based recommender systems at scale (100M+ users or items), ideally within travel, personalization, or e-commerce contexts Finetuning PhD focused on topics related to aligning Large Language Models for recommendation and search tasks via Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), or Direct Preference Optimization (DPO) Experience deploying a fine-tuned large language model optimized for serving performance Proven ability to design and lead small-scale research initiatives, guiding junior researchers or engineers through experimental design and evaluation 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. Cambridge, MA: $262,500 - $299,600 for Applied Researcher 5 McLean, VA: $262,500 - $299,600 for Applied Researcher 5 New York, NY: $286,400 - $326,800 for Applied Researcher 5 San Francisco, CA: $286,400 - $326,800 for Applied Researcher 5 San Jose, CA: $286,400 - $326,800 for Applied Researcher 5 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 . click apply for full job details
Applied Researcher 5 At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, Capital One has been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to building world-class applied science and engineering teams and continue our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work touches every aspect of the research life cycle, from partnering with Academia to building production systems. We work with product, technology and business leaders to apply the state of the art in AI to our business. What You'll Do: Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact with their money. Leverage a broad stack of technologies; Pytorch, AWS Ultraclusters, Huggingface, Lightning, and more, to reveal the insights hidden within huge volumes of numeric and textual data. Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences. Flex your interpersonal skills to translate the complexity of your work into tangible business goals. Lead cross-functional research threads that bridge prototype model development and deployment with ML engineering partners, ensuring scientific insights translate to production impact The Ideal Candidate: You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it's about making the right decision for our customers Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You're not afraid to share a new idea A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You're passionate about talent development for your own team and beyond Technical. You're comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms Has a deep understanding of the foundations of AI methodologies Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF. An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes Experience in delivering libraries, platform level code or solution level code to existing products A professional with a track record of coming up with high quality ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects This role is designated as a hybrid position. You are expected to adhere to Capital One's hybrid policy regarding required on-site presence at your designated office location. Basic Qualifications: Currently has, or is in the process of obtaining, PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with an exception that required degree will be obtained on or before the scheduled start date plus 2 years of experience in Applied Research or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: PhD in Computer Science, Machine Learning, High Performance Computing, Applied Mathematics, Electrical Engineering or related fields LLM PhD focus on Generative AI (LLMs, Diffusion models and related approaches) or Masters with 5 years of industrial research experience in deep learning based techniques Multiple publications at top-tier venues (e.g., KDD, SIGIR, RecSys, NeurIPS, ICLR) on topics related to generative recommendation, retrieval-augmented generation (RAG), LLM-based agents, or large-scale semantic search Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens) Behavioral Models PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series) Multiple papers on topics relevant to training models on graph and sequential data structures at KDD, ICML, NeurIPs, ICLR Experience training foundation models from scratch or significantly adapting large-scale models (10B+ parameters) for complex financial data, transactional sequences, or user behavior graphs Track record of scaling graph models to greater than 50M nodes and operating within high-velocity, real-time streaming data environments Contributions to common open source frameworks (pytorch-geometric, DGL) Proposed new methods for inference or representation learning on graphs or sequences Worked datasets with 100m+ users Optimization (Training & Inference) Multiple years of experience or publications focused on model sparsification, quantization, training parallelism/partitioning design, or gradient checkpointing for 10B+ parameter models Hands-on experience with large-scale orchestration systems (Kubernetes, KubeRay) and contributions to common open-source ML frameworks (PyTorch-Geometric, DGL, HuggingFace) Experience building and deploying deep learning-based recommender systems at scale (100M+ users or items), ideally within travel, personalization, or e-commerce contexts Finetuning PhD focused on topics related to aligning Large Language Models for recommendation and search tasks via Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), or Direct Preference Optimization (DPO) Experience deploying a fine-tuned large language model optimized for serving performance Proven ability to design and lead small-scale research initiatives, guiding junior researchers or engineers through experimental design and evaluation 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. Cambridge, MA: $262,500 - $299,600 for Applied Researcher 5 McLean, VA: $262,500 - $299,600 for Applied Researcher 5 New York, NY: $286,400 - $326,800 for Applied Researcher 5 San Francisco, CA: $286,400 - $326,800 for Applied Researcher 5 San Jose, CA: $286,400 - $326,800 for Applied Researcher 5 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 . click apply for full job details
11/13/2026
Full time
Applied Researcher 5 At Capital One, we are creating trustworthy and reliable AI systems, changing banking for good. For years, Capital One has been leading the industry in using machine learning to create real-time, intelligent, automated customer experiences. From informing customers about unusual charges to answering their questions in real time, our applications of AI & ML are bringing humanity and simplicity to banking. We are committed to building world-class applied science and engineering teams and continue our industry leading capabilities with breakthrough product experiences and scalable, high-performance AI infrastructure. At Capital One, you will help bring the transformative power of emerging AI capabilities to reimagine how we serve our customers and businesses who have come to love the products and services we build. Team Description: The AI Foundations team is at the center of bringing our vision for AI at Capital One to life. Our work touches every aspect of the research life cycle, from partnering with Academia to building production systems. We work with product, technology and business leaders to apply the state of the art in AI to our business. What You'll Do: Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI-powered products that change how customers interact with their money. Leverage a broad stack of technologies; Pytorch, AWS Ultraclusters, Huggingface, Lightning, and more, to reveal the insights hidden within huge volumes of numeric and textual data. Build AI foundation models through all phases of development, from design through training, evaluation, validation, and implementation. Engage in high impact applied research to take the latest AI developments and push them into the next generation of customer experiences. Flex your interpersonal skills to translate the complexity of your work into tangible business goals. Lead cross-functional research threads that bridge prototype model development and deployment with ML engineering partners, ensuring scientific insights translate to production impact The Ideal Candidate: You love the process of analyzing and creating, but also share our passion to do the right thing. You know at the end of the day it's about making the right decision for our customers Innovative. You continually research and evaluate emerging technologies. You stay current on published state-of-the-art methods, technologies, and applications and seek out opportunities to apply them Creative. You thrive on bringing definition to big, undefined problems. You love asking questions and pushing hard to find answers. You're not afraid to share a new idea A leader. You challenge conventional thinking and work with stakeholders to identify and improve the status quo. You're passionate about talent development for your own team and beyond Technical. You're comfortable with open-source languages and are passionate about developing further. You have hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms Has a deep understanding of the foundations of AI methodologies Experience building large deep learning models, whether on language, images, events, or graphs, as well as expertise in one or more of the following: training optimization, self-supervised learning, robustness, explainability, RLHF. An engineering mindset as shown by a track record of delivering models at scale both in terms of training data and inference volumes Experience in delivering libraries, platform level code or solution level code to existing products A professional with a track record of coming up with high quality ideas or improving upon existing ideas in machine learning, demonstrated by accomplishments such as first author publications or projects Possess the ability to own and pursue a research agenda, including choosing impactful research problems and autonomously carrying out long-running projects This role is designated as a hybrid position. You are expected to adhere to Capital One's hybrid policy regarding required on-site presence at your designated office location. Basic Qualifications: Currently has, or is in the process of obtaining, PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields, with an exception that required degree will be obtained on or before the scheduled start date plus 2 years of experience in Applied Research or M.S. in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or related fields plus 4 years of experience in Applied Research Preferred Qualifications: PhD in Computer Science, Machine Learning, High Performance Computing, Applied Mathematics, Electrical Engineering or related fields LLM PhD focus on Generative AI (LLMs, Diffusion models and related approaches) or Masters with 5 years of industrial research experience in deep learning based techniques Multiple publications at top-tier venues (e.g., KDD, SIGIR, RecSys, NeurIPS, ICLR) on topics related to generative recommendation, retrieval-augmented generation (RAG), LLM-based agents, or large-scale semantic search Member of team that has trained a large language model from scratch (10B + parameters, 500B+ tokens) Behavioral Models PhD focus on topics in geometric deep learning (Graph Neural Networks, Sequential Models, Multivariate Time Series) Multiple papers on topics relevant to training models on graph and sequential data structures at KDD, ICML, NeurIPs, ICLR Experience training foundation models from scratch or significantly adapting large-scale models (10B+ parameters) for complex financial data, transactional sequences, or user behavior graphs Track record of scaling graph models to greater than 50M nodes and operating within high-velocity, real-time streaming data environments Contributions to common open source frameworks (pytorch-geometric, DGL) Proposed new methods for inference or representation learning on graphs or sequences Worked datasets with 100m+ users Optimization (Training & Inference) Multiple years of experience or publications focused on model sparsification, quantization, training parallelism/partitioning design, or gradient checkpointing for 10B+ parameter models Hands-on experience with large-scale orchestration systems (Kubernetes, KubeRay) and contributions to common open-source ML frameworks (PyTorch-Geometric, DGL, HuggingFace) Experience building and deploying deep learning-based recommender systems at scale (100M+ users or items), ideally within travel, personalization, or e-commerce contexts Finetuning PhD focused on topics related to aligning Large Language Models for recommendation and search tasks via Supervised Fine-Tuning (SFT), Reinforcement Learning from Human Feedback (RLHF), or Direct Preference Optimization (DPO) Experience deploying a fine-tuned large language model optimized for serving performance Proven ability to design and lead small-scale research initiatives, guiding junior researchers or engineers through experimental design and evaluation 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. Cambridge, MA: $262,500 - $299,600 for Applied Researcher 5 McLean, VA: $262,500 - $299,600 for Applied Researcher 5 New York, NY: $286,400 - $326,800 for Applied Researcher 5 San Francisco, CA: $286,400 - $326,800 for Applied Researcher 5 San Jose, CA: $286,400 - $326,800 for Applied Researcher 5 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 . click apply for full job details
Job Description Job Description About Miebach Miebach Consulting is one of the world's leading supply chain-focused consulting and engineering firms. For over 50 years, we have helped clients design, optimize, and implement end-to-end supply chain solutions across industries including consumer goods, food, pharma, retail, and high-tech. Our global expertise spans Supply Chain Transformation, Engineering & Operational Excellence, and Digitalization. We are proud to be recognized among SupplyChainBrain's Top 100 Supply Chain Partners, with many of Gartner's Top 25 Supply Chains relying on Miebach to support their strategic initiatives. About the role We are seeking a Process Mining Technical Analyst to provide technical support throughout process mining projects. The role focuses on data preparation, event-log development, process mining configuration, data validation, troubleshooting, and analytical support. The successful candidate must understand process and material flows, particularly within warehouse, transportation, manufacturing, or supply chain environments. They will work closely with process mining consultants, data engineers, project managers, and client teams to ensure that the required data, process logic, KPIs, and dashboards are correctly developed and validated. The role may contribute to identifying process issues and improvement opportunities, but developing business recommendations is not the primary responsibility. Key Responsibilities Project Collaboration and Delivery Work collaboratively as an active member of multidisciplinary project teams. Support process mining projects from data discovery through delivery and handover. Participate in project meetings, workshops, working sessions, and client reviews. Provide regular updates on progress, risks, dependencies, and open technical topics. Process Understanding and Requirements Understand and document operational processes and material flows. Translate business and process requirements into technical data requirements. Identify process objects, activities, timestamps, attributes, and relationships. Support data mapping between source systems and the process mining platform. Data Preparation and Process Models Extract, clean, transform, and prepare data from WMS, TMS, ERP, MES, YMS, and other systems. Build and maintain event logs and process mining data models. Validate data, process logic, calculations, and analytical outputs. Investigate data-quality issues, missing events, inconsistent master data, and unexpected results. Process Mining Analysis and Insights Configure process mining analyses, KPIs, dashboards, filters, and process views. Contribute to the identification of process deviations, bottlenecks, and improvement opportunities when required. Support the preparation of project deliverables, demonstrations, and client presentations. Testing and Technical Troubleshooting Troubleshoot technical issues and coordinate resolution with the project team, IT teams, and platform specialists. Prepare test cases and support system testing and user acceptance testing. Assist with validating system functionality, data connections, and analytical outputs. Documentation and Knowledge Management Maintain technical documentation, data dictionaries, assumptions logs, and configuration records. Document data sources, transformations, process logic, testing results, and relevant project decisions. Qualifications Bachelor's degree in Industrial Engineering, Supply Chain, Data Analytics, Information Systems, Computer Science, or a related field 2-3 years of experience in data analysis, business intelligence, process mining, systems analysis, or technical consulting Strong understanding of process flows and material flows Experience working with transactional or operational data Practical experience with SQL and data preparation Strong attention to detail and ability to validate complex data relationships Ability to understand system transactions and connect them to real operational activities Strong troubleshooting and problem-solving skills Good written and verbal communication skills Ability to work effectively with both technical and business stakeholders Technical Skills Strong SQL skills, including joins, aggregations, window functions, and data-quality checks Understanding of data modeling, relational databases, and ETL/ELT concepts Strong Excel skills for data analysis, validation, and reconciliation Experience working with transactional and operational data Ability to understand data structures and connect system transactions to operational and material flows Experience with PQL, particularly when working with Celonis, is preferred Familiarity with process-mining platforms such as Celonis, SAP Signavio Process Intelligence, UiPath Process Mining, IBM Process Mining, or Apromore is preferred; experience with one or more platforms is sufficient Experience with Power BI or Tableau is preferred Familiarity with AI, machine learning, or advanced analytics in process-mining work-such as anomaly detection, root-cause analysis, or predictive insights-is a plus Exposure to APIs, cloud platforms, or data lakes is an asset Core Competencies Understanding of operational and material flows Technical data analysis Data quality and validation Structured troubleshooting Attention to detail Documentation and organization Collaboration and communication Ability to learn new systems and technologies Reliability and ownership Typical Deliverables Data requirements and source-to-target mappings Data dictionaries and process-flow documentation Event logs and process mining data models KPI calculations and technical definitions Dashboards and analytical views Data-quality assessments Test scripts and validation results Technical documentation and configuration records Support materials for workshops and client presentations Why Join Miebach? Flexible work hours & hybrid/remote options Competitive compensation + performance-based bonuses Comprehensive health benefits (medical, dental, vision) Career development programs & international training Flexible PTO, generous parental leave, and paid holidays 401(k) contributions & financial wellness programs PIa69d29723c4e-9205
11/12/2026
Full time
Job Description Job Description About Miebach Miebach Consulting is one of the world's leading supply chain-focused consulting and engineering firms. For over 50 years, we have helped clients design, optimize, and implement end-to-end supply chain solutions across industries including consumer goods, food, pharma, retail, and high-tech. Our global expertise spans Supply Chain Transformation, Engineering & Operational Excellence, and Digitalization. We are proud to be recognized among SupplyChainBrain's Top 100 Supply Chain Partners, with many of Gartner's Top 25 Supply Chains relying on Miebach to support their strategic initiatives. About the role We are seeking a Process Mining Technical Analyst to provide technical support throughout process mining projects. The role focuses on data preparation, event-log development, process mining configuration, data validation, troubleshooting, and analytical support. The successful candidate must understand process and material flows, particularly within warehouse, transportation, manufacturing, or supply chain environments. They will work closely with process mining consultants, data engineers, project managers, and client teams to ensure that the required data, process logic, KPIs, and dashboards are correctly developed and validated. The role may contribute to identifying process issues and improvement opportunities, but developing business recommendations is not the primary responsibility. Key Responsibilities Project Collaboration and Delivery Work collaboratively as an active member of multidisciplinary project teams. Support process mining projects from data discovery through delivery and handover. Participate in project meetings, workshops, working sessions, and client reviews. Provide regular updates on progress, risks, dependencies, and open technical topics. Process Understanding and Requirements Understand and document operational processes and material flows. Translate business and process requirements into technical data requirements. Identify process objects, activities, timestamps, attributes, and relationships. Support data mapping between source systems and the process mining platform. Data Preparation and Process Models Extract, clean, transform, and prepare data from WMS, TMS, ERP, MES, YMS, and other systems. Build and maintain event logs and process mining data models. Validate data, process logic, calculations, and analytical outputs. Investigate data-quality issues, missing events, inconsistent master data, and unexpected results. Process Mining Analysis and Insights Configure process mining analyses, KPIs, dashboards, filters, and process views. Contribute to the identification of process deviations, bottlenecks, and improvement opportunities when required. Support the preparation of project deliverables, demonstrations, and client presentations. Testing and Technical Troubleshooting Troubleshoot technical issues and coordinate resolution with the project team, IT teams, and platform specialists. Prepare test cases and support system testing and user acceptance testing. Assist with validating system functionality, data connections, and analytical outputs. Documentation and Knowledge Management Maintain technical documentation, data dictionaries, assumptions logs, and configuration records. Document data sources, transformations, process logic, testing results, and relevant project decisions. Qualifications Bachelor's degree in Industrial Engineering, Supply Chain, Data Analytics, Information Systems, Computer Science, or a related field 2-3 years of experience in data analysis, business intelligence, process mining, systems analysis, or technical consulting Strong understanding of process flows and material flows Experience working with transactional or operational data Practical experience with SQL and data preparation Strong attention to detail and ability to validate complex data relationships Ability to understand system transactions and connect them to real operational activities Strong troubleshooting and problem-solving skills Good written and verbal communication skills Ability to work effectively with both technical and business stakeholders Technical Skills Strong SQL skills, including joins, aggregations, window functions, and data-quality checks Understanding of data modeling, relational databases, and ETL/ELT concepts Strong Excel skills for data analysis, validation, and reconciliation Experience working with transactional and operational data Ability to understand data structures and connect system transactions to operational and material flows Experience with PQL, particularly when working with Celonis, is preferred Familiarity with process-mining platforms such as Celonis, SAP Signavio Process Intelligence, UiPath Process Mining, IBM Process Mining, or Apromore is preferred; experience with one or more platforms is sufficient Experience with Power BI or Tableau is preferred Familiarity with AI, machine learning, or advanced analytics in process-mining work-such as anomaly detection, root-cause analysis, or predictive insights-is a plus Exposure to APIs, cloud platforms, or data lakes is an asset Core Competencies Understanding of operational and material flows Technical data analysis Data quality and validation Structured troubleshooting Attention to detail Documentation and organization Collaboration and communication Ability to learn new systems and technologies Reliability and ownership Typical Deliverables Data requirements and source-to-target mappings Data dictionaries and process-flow documentation Event logs and process mining data models KPI calculations and technical definitions Dashboards and analytical views Data-quality assessments Test scripts and validation results Technical documentation and configuration records Support materials for workshops and client presentations Why Join Miebach? Flexible work hours & hybrid/remote options Competitive compensation + performance-based bonuses Comprehensive health benefits (medical, dental, vision) Career development programs & international training Flexible PTO, generous parental leave, and paid holidays 401(k) contributions & financial wellness programs PIa69d29723c4e-9205
About Miebach Miebach Consulting is one of the world's leading supply chain-focused consulting and engineering firms. For over 50 years, we have helped clients design, optimize, and implement end-to-end supply chain solutions across industries including consumer goods, food, pharma, retail, and high-tech. Our global expertise spans Supply Chain Transformation, Engineering & Operational Excellence, and Digitalization. We are proud to be recognized among SupplyChainBrain's Top 100 Supply Chain Partners, with many of Gartner's Top 25 Supply Chains relying on Miebach to support their strategic initiatives. About the role We are seeking a Process Mining Technical Analyst to provide technical support throughout process mining projects. The role focuses on data preparation, event-log development, process mining configuration, data validation, troubleshooting, and analytical support. The successful candidate must understand process and material flows, particularly within warehouse, transportation, manufacturing, or supply chain environments. They will work closely with process mining consultants, data engineers, project managers, and client teams to ensure that the required data, process logic, KPIs, and dashboards are correctly developed and validated. The role may contribute to identifying process issues and improvement opportunities, but developing business recommendations is not the primary responsibility. Key Responsibilities Project Collaboration and Delivery Work collaboratively as an active member of multidisciplinary project teams. Support process mining projects from data discovery through delivery and handover. Participate in project meetings, workshops, working sessions, and client reviews. Provide regular updates on progress, risks, dependencies, and open technical topics. Process Understanding and Requirements Understand and document operational processes and material flows. Translate business and process requirements into technical data requirements. Identify process objects, activities, timestamps, attributes, and relationships. Support data mapping between source systems and the process mining platform. Data Preparation and Process Models Extract, clean, transform, and prepare data from WMS, TMS, ERP, MES, YMS, and other systems. Build and maintain event logs and process mining data models. Validate data, process logic, calculations, and analytical outputs. Investigate data-quality issues, missing events, inconsistent master data, and unexpected results. Process Mining Analysis and Insights Configure process mining analyses, KPIs, dashboards, filters, and process views. Contribute to the identification of process deviations, bottlenecks, and improvement opportunities when required. Support the preparation of project deliverables, demonstrations, and client presentations. Testing and Technical Troubleshooting Troubleshoot technical issues and coordinate resolution with the project team, IT teams, and platform specialists. Prepare test cases and support system testing and user acceptance testing. Assist with validating system functionality, data connections, and analytical outputs. Documentation and Knowledge Management Maintain technical documentation, data dictionaries, assumptions logs, and configuration records. Document data sources, transformations, process logic, testing results, and relevant project decisions. Qualifications Bachelor's degree in Industrial Engineering, Supply Chain, Data Analytics, Information Systems, Computer Science, or a related field 2-3 years of experience in data analysis, business intelligence, process mining, systems analysis, or technical consulting Strong understanding of process flows and material flows Experience working with transactional or operational data Practical experience with SQL and data preparation Strong attention to detail and ability to validate complex data relationships Ability to understand system transactions and connect them to real operational activities Strong troubleshooting and problem-solving skills Good written and verbal communication skills Ability to work effectively with both technical and business stakeholders Technical Skills Strong SQL skills, including joins, aggregations, window functions, and data-quality checks Understanding of data modeling, relational databases, and ETL/ELT concepts Strong Excel skills for data analysis, validation, and reconciliation Experience working with transactional and operational data Ability to understand data structures and connect system transactions to operational and material flows Experience with PQL, particularly when working with Celonis, is preferred Familiarity with process-mining platforms such as Celonis, SAP Signavio Process Intelligence, UiPath Process Mining, IBM Process Mining, or Apromore is preferred; experience with one or more platforms is sufficient Experience with Power BI or Tableau is preferred Familiarity with AI, machine learning, or advanced analytics in process-mining work-such as anomaly detection, root-cause analysis, or predictive insights-is a plus Exposure to APIs, cloud platforms, or data lakes is an asset Core Competencies Understanding of operational and material flows Technical data analysis Data quality and validation Structured troubleshooting Attention to detail Documentation and organization Collaboration and communication Ability to learn new systems and technologies Reliability and ownership Typical Deliverables Data requirements and source-to-target mappings Data dictionaries and process-flow documentation Event logs and process mining data models KPI calculations and technical definitions Dashboards and analytical views Data-quality assessments Test scripts and validation results Technical documentation and configuration records Support materials for workshops and client presentations Why Join Miebach? Flexible work hours & hybrid/remote options Competitive compensation + performance-based bonuses Comprehensive health benefits (medical, dental, vision) Career development programs & international training Flexible PTO, generous parental leave, and paid holidays 401(k) contributions & financial wellness programs PI8211be5-
11/02/2026
Full time
About Miebach Miebach Consulting is one of the world's leading supply chain-focused consulting and engineering firms. For over 50 years, we have helped clients design, optimize, and implement end-to-end supply chain solutions across industries including consumer goods, food, pharma, retail, and high-tech. Our global expertise spans Supply Chain Transformation, Engineering & Operational Excellence, and Digitalization. We are proud to be recognized among SupplyChainBrain's Top 100 Supply Chain Partners, with many of Gartner's Top 25 Supply Chains relying on Miebach to support their strategic initiatives. About the role We are seeking a Process Mining Technical Analyst to provide technical support throughout process mining projects. The role focuses on data preparation, event-log development, process mining configuration, data validation, troubleshooting, and analytical support. The successful candidate must understand process and material flows, particularly within warehouse, transportation, manufacturing, or supply chain environments. They will work closely with process mining consultants, data engineers, project managers, and client teams to ensure that the required data, process logic, KPIs, and dashboards are correctly developed and validated. The role may contribute to identifying process issues and improvement opportunities, but developing business recommendations is not the primary responsibility. Key Responsibilities Project Collaboration and Delivery Work collaboratively as an active member of multidisciplinary project teams. Support process mining projects from data discovery through delivery and handover. Participate in project meetings, workshops, working sessions, and client reviews. Provide regular updates on progress, risks, dependencies, and open technical topics. Process Understanding and Requirements Understand and document operational processes and material flows. Translate business and process requirements into technical data requirements. Identify process objects, activities, timestamps, attributes, and relationships. Support data mapping between source systems and the process mining platform. Data Preparation and Process Models Extract, clean, transform, and prepare data from WMS, TMS, ERP, MES, YMS, and other systems. Build and maintain event logs and process mining data models. Validate data, process logic, calculations, and analytical outputs. Investigate data-quality issues, missing events, inconsistent master data, and unexpected results. Process Mining Analysis and Insights Configure process mining analyses, KPIs, dashboards, filters, and process views. Contribute to the identification of process deviations, bottlenecks, and improvement opportunities when required. Support the preparation of project deliverables, demonstrations, and client presentations. Testing and Technical Troubleshooting Troubleshoot technical issues and coordinate resolution with the project team, IT teams, and platform specialists. Prepare test cases and support system testing and user acceptance testing. Assist with validating system functionality, data connections, and analytical outputs. Documentation and Knowledge Management Maintain technical documentation, data dictionaries, assumptions logs, and configuration records. Document data sources, transformations, process logic, testing results, and relevant project decisions. Qualifications Bachelor's degree in Industrial Engineering, Supply Chain, Data Analytics, Information Systems, Computer Science, or a related field 2-3 years of experience in data analysis, business intelligence, process mining, systems analysis, or technical consulting Strong understanding of process flows and material flows Experience working with transactional or operational data Practical experience with SQL and data preparation Strong attention to detail and ability to validate complex data relationships Ability to understand system transactions and connect them to real operational activities Strong troubleshooting and problem-solving skills Good written and verbal communication skills Ability to work effectively with both technical and business stakeholders Technical Skills Strong SQL skills, including joins, aggregations, window functions, and data-quality checks Understanding of data modeling, relational databases, and ETL/ELT concepts Strong Excel skills for data analysis, validation, and reconciliation Experience working with transactional and operational data Ability to understand data structures and connect system transactions to operational and material flows Experience with PQL, particularly when working with Celonis, is preferred Familiarity with process-mining platforms such as Celonis, SAP Signavio Process Intelligence, UiPath Process Mining, IBM Process Mining, or Apromore is preferred; experience with one or more platforms is sufficient Experience with Power BI or Tableau is preferred Familiarity with AI, machine learning, or advanced analytics in process-mining work-such as anomaly detection, root-cause analysis, or predictive insights-is a plus Exposure to APIs, cloud platforms, or data lakes is an asset Core Competencies Understanding of operational and material flows Technical data analysis Data quality and validation Structured troubleshooting Attention to detail Documentation and organization Collaboration and communication Ability to learn new systems and technologies Reliability and ownership Typical Deliverables Data requirements and source-to-target mappings Data dictionaries and process-flow documentation Event logs and process mining data models KPI calculations and technical definitions Dashboards and analytical views Data-quality assessments Test scripts and validation results Technical documentation and configuration records Support materials for workshops and client presentations Why Join Miebach? Flexible work hours & hybrid/remote options Competitive compensation + performance-based bonuses Comprehensive health benefits (medical, dental, vision) Career development programs & international training Flexible PTO, generous parental leave, and paid holidays 401(k) contributions & financial wellness programs PI8211be5-
Job Description Job Description Company Description About AbbVie AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at . on LinkedIn, Facebook, Instagram, X and YouTube. Job Description While the AI innovation race in Biopharma is focused on Drug discovery, Product Development/ CMC represents the next barrier/ bottleneck. The complexity of biological systems, the rigor of regulatory expectations, the pace of pipeline growth, and the enormous value at stake make this one of the highest-leverage domains for applied data science and AI in the entire pharmaceutical value chain. We here at BTS - PDST, are building a dedicated, AI-native team that is driving cutting edge programs across early stage, late stage and commercial product development to accelerate E2E product development and launch, maximize yields of block buster products. Through our deep collaboration with PDST scientists we are boldly reimagining how Abbvie can bring our pipeline products and lifesaving drugs to patients faster, safer and in cost effective manner fueled by AI. Principal Data Engineer is a highly technical, AI-native role responsible for designing, building, and operating production-grade data pipelines and data products that power AI/ML, analytics, and automation across AbbVie's CMC and manufacturing ecosystem. This role is embedded inside PDST and works at the frontier of pharmaceutical data engineering. You will integrate and harmonize data from the full spectrum of manufacturing and development systems - including MES, historians, LIMS, QMS, ERP, and instrument platforms - and transform it into reliable, governed, semantically rich data assets that data scientists, process engineers, and AI systems can actually use. Enterprise-scale scope: Enterprise-scale biologics portfolio spanning clinical, commercial, and lifecycle stages Building AI playbook for the future: First-in-AbbVie and first-in-biologics analytical approaches; you build the AI playbook for the future Growth and Impact: Direct impact on regulatory submissions, commercial readiness, and manufacturing decisions through deep cross-functional exposure to manufacturing, quality, regulatory, and scientific leadership Mission: Every model you build helps ensure safe, reliable medicines reach patients at scale Responsibilities Data Ingestion & Integration Design and implement scalable, robust data ingestion pipelines that connect CMC and manufacturing source systems - including MES (Manufacturing Execution Systems), process historians, LIMS, QMS, ERP platforms, and instrument data sources - to centralized and federated data environments. Build connectors, adapters, and integration layers that handle the heterogeneous data formats, protocols, and latency profiles characteristic of pharmaceutical manufacturing environments. Support both batch and real-time/streaming data patterns, selecting appropriate architectures based on use case requirements. Data Harmonization & Semantic Modeling Develop and maintain harmonized data models and ontologies that bring consistency to CMC and manufacturing data across sites, systems, and modalities. Execute semantic mapping efforts that align source system fields, units, and identifiers to enterprise data standards and scientific meaning. Collaborate with process scientists, analytical chemists, and manufacturing engineers to ensure data models accurately reflect domain reality. Data Quality, Observability & Governance Implement automated data quality controls, validation frameworks, and anomaly detection mechanisms across pipeline layers. Build and maintain data lineage documentation and metadata infrastructure, enabling full traceability from source system to AI model input. Establish pipeline observability practices - monitoring, alerting, SLA tracking - to ensure data product reliability in production. Support data governance practices aligned with GxP requirements, 21 CFR Part 11, and AbbVie data standards. AI/ML Enablement & Data Product Development Architect and deliver governed, versioned, reusable data products purpose-built for AI/ML consumption, including feature stores, curated datasets, and vector-ready data layers for RAG and LLM applications. Partner closely with data scientists, ML engineers, and process modelers to understand model data requirements and translate them into reliable, scalable data infrastructure. Accelerate AI program delivery by eliminating data bottlenecks - not by workarounds, but by solving root causes structurally. Platform & Operational Enablement Contribute to the design and evolution of PDST's cloud-based data platform, including lakehouse architecture, data cataloging, access control, and compute infrastructure. Write and maintain infrastructure-as-code, CI/CD pipelines, and automated testing frameworks for data systems. Support platform onboarding of new CMC data domains and manufacturing sites, ensuring consistent application of standards and patterns. Provide operational support for production data pipelines, maintaining uptime and data freshness commitments. Stakeholder Engagement & Scientific Leadership Influence technical decision-making without formal authority - earning trust through scientific rigor, transparent methodology, and demonstrated business impact. Qualifications Required: Bachelor's Degree Computer Science, Data Engineering, Information Systems, Software Engineering, Bioinformatics, or a closely related technical field plus 6 years' experience; Master's Degree plus 5 years' experience; PhD plus 0 years' experience. Respective years of hands-on experience designing and building enterprise-grade data pipelines, integration workflows, and data products in complex, multi-source environments. Expert-level proficiency in Python for data engineering tasks - pipeline development, transformation logic, data validation, and automation. Strong SQL skills across modern analytical and transactional databases; comfort with both ANSI SQL and platform-specific dialects. Demonstrated experience with cloud data platforms (AWS, Azure, or GCP) and modern data stack components - including tools such as dbt, Spark, Airflow, Databricks, Snowflake, or equivalents. Develop ETL/ELT pipelines using tools such as Informatica, Talend, Apache NiFi, and cloud-native services (e.g., AWS Glue, Azure Data Factory). Implement master data management (MDM), metadata management, and data cataloging solutions to ensure proper data lineage, accessibility, and compliance. Set and enforce standards for API development and data integration (REST, GraphQL, OData), enabling seamless integration using microservices architectures. Ownership orientation: you define your own problem space, drive solutions to completion, and hold yourself accountable to outcomes - not just outputs. Solution-architect instinct: you think before you build, consider the full landscape of available approaches, and choose tools based on fit-for-purpose reasoning rather than familiarity or trend. Scientific integrity: you build models you can explain, defend, and improve - and you apply the same standard to the work of others. Influence through credibility: you earn the confidence of scientists, engineers, and quality professionals by being right, being clear, and being useful - not by title or volume. Bias for impact: you are drawn to problems where the stakes are high and the analytical opportunity is real, and you are energized rather than intimidated by ambiguity. Preferred: 3+ years of hands-on experience designing and building enterprise-grade data pipelines, integration workflows, and data products in complex, multi-source environments. Familiarity with technology transfer workflows, process characterization study design, or commercial process validation (PPQ/PV) in a biologics or pharmaceutical context. Experience in pharmaceutical, biotech, or other regulated life sciences manufacturing environments. Familiarity with GxP data principles, 21 CFR Part 11 compliance, or data integrity requirements in regulated industries. Prior exposure to manufacturing source systems such as MES, process historians (e.g., OSIsoft PI/AVEVA), LIMS, QMS, or ERP platforms Experience building data infrastructure for AI/ML programs - including feature engineering pipelines, model training datasets, or vector/embedding data layers for RAG architectures. Knowledge of biologics manufacturing processes (e.g., upstream cell culture, downstream purification, fill-finish) or CMC development workflows. Familiarity with data mesh, data fabric, or federated data architecture patterns. Experience with graph databases, knowledge graphs, or ontology frameworks applied to scientific or manufacturing data. Contributions to open-source data tooling or demonstrated engagement with the modern data engineering community. Design logical, physical, and conceptual data models using modeling tools (e.g., Erwin, PowerDesigner, dbt). . click apply for full job details
09/26/2026
Full time
Job Description Job Description Company Description About AbbVie AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at . on LinkedIn, Facebook, Instagram, X and YouTube. Job Description While the AI innovation race in Biopharma is focused on Drug discovery, Product Development/ CMC represents the next barrier/ bottleneck. The complexity of biological systems, the rigor of regulatory expectations, the pace of pipeline growth, and the enormous value at stake make this one of the highest-leverage domains for applied data science and AI in the entire pharmaceutical value chain. We here at BTS - PDST, are building a dedicated, AI-native team that is driving cutting edge programs across early stage, late stage and commercial product development to accelerate E2E product development and launch, maximize yields of block buster products. Through our deep collaboration with PDST scientists we are boldly reimagining how Abbvie can bring our pipeline products and lifesaving drugs to patients faster, safer and in cost effective manner fueled by AI. Principal Data Engineer is a highly technical, AI-native role responsible for designing, building, and operating production-grade data pipelines and data products that power AI/ML, analytics, and automation across AbbVie's CMC and manufacturing ecosystem. This role is embedded inside PDST and works at the frontier of pharmaceutical data engineering. You will integrate and harmonize data from the full spectrum of manufacturing and development systems - including MES, historians, LIMS, QMS, ERP, and instrument platforms - and transform it into reliable, governed, semantically rich data assets that data scientists, process engineers, and AI systems can actually use. Enterprise-scale scope: Enterprise-scale biologics portfolio spanning clinical, commercial, and lifecycle stages Building AI playbook for the future: First-in-AbbVie and first-in-biologics analytical approaches; you build the AI playbook for the future Growth and Impact: Direct impact on regulatory submissions, commercial readiness, and manufacturing decisions through deep cross-functional exposure to manufacturing, quality, regulatory, and scientific leadership Mission: Every model you build helps ensure safe, reliable medicines reach patients at scale Responsibilities Data Ingestion & Integration Design and implement scalable, robust data ingestion pipelines that connect CMC and manufacturing source systems - including MES (Manufacturing Execution Systems), process historians, LIMS, QMS, ERP platforms, and instrument data sources - to centralized and federated data environments. Build connectors, adapters, and integration layers that handle the heterogeneous data formats, protocols, and latency profiles characteristic of pharmaceutical manufacturing environments. Support both batch and real-time/streaming data patterns, selecting appropriate architectures based on use case requirements. Data Harmonization & Semantic Modeling Develop and maintain harmonized data models and ontologies that bring consistency to CMC and manufacturing data across sites, systems, and modalities. Execute semantic mapping efforts that align source system fields, units, and identifiers to enterprise data standards and scientific meaning. Collaborate with process scientists, analytical chemists, and manufacturing engineers to ensure data models accurately reflect domain reality. Data Quality, Observability & Governance Implement automated data quality controls, validation frameworks, and anomaly detection mechanisms across pipeline layers. Build and maintain data lineage documentation and metadata infrastructure, enabling full traceability from source system to AI model input. Establish pipeline observability practices - monitoring, alerting, SLA tracking - to ensure data product reliability in production. Support data governance practices aligned with GxP requirements, 21 CFR Part 11, and AbbVie data standards. AI/ML Enablement & Data Product Development Architect and deliver governed, versioned, reusable data products purpose-built for AI/ML consumption, including feature stores, curated datasets, and vector-ready data layers for RAG and LLM applications. Partner closely with data scientists, ML engineers, and process modelers to understand model data requirements and translate them into reliable, scalable data infrastructure. Accelerate AI program delivery by eliminating data bottlenecks - not by workarounds, but by solving root causes structurally. Platform & Operational Enablement Contribute to the design and evolution of PDST's cloud-based data platform, including lakehouse architecture, data cataloging, access control, and compute infrastructure. Write and maintain infrastructure-as-code, CI/CD pipelines, and automated testing frameworks for data systems. Support platform onboarding of new CMC data domains and manufacturing sites, ensuring consistent application of standards and patterns. Provide operational support for production data pipelines, maintaining uptime and data freshness commitments. Stakeholder Engagement & Scientific Leadership Influence technical decision-making without formal authority - earning trust through scientific rigor, transparent methodology, and demonstrated business impact. Qualifications Required: Bachelor's Degree Computer Science, Data Engineering, Information Systems, Software Engineering, Bioinformatics, or a closely related technical field plus 6 years' experience; Master's Degree plus 5 years' experience; PhD plus 0 years' experience. Respective years of hands-on experience designing and building enterprise-grade data pipelines, integration workflows, and data products in complex, multi-source environments. Expert-level proficiency in Python for data engineering tasks - pipeline development, transformation logic, data validation, and automation. Strong SQL skills across modern analytical and transactional databases; comfort with both ANSI SQL and platform-specific dialects. Demonstrated experience with cloud data platforms (AWS, Azure, or GCP) and modern data stack components - including tools such as dbt, Spark, Airflow, Databricks, Snowflake, or equivalents. Develop ETL/ELT pipelines using tools such as Informatica, Talend, Apache NiFi, and cloud-native services (e.g., AWS Glue, Azure Data Factory). Implement master data management (MDM), metadata management, and data cataloging solutions to ensure proper data lineage, accessibility, and compliance. Set and enforce standards for API development and data integration (REST, GraphQL, OData), enabling seamless integration using microservices architectures. Ownership orientation: you define your own problem space, drive solutions to completion, and hold yourself accountable to outcomes - not just outputs. Solution-architect instinct: you think before you build, consider the full landscape of available approaches, and choose tools based on fit-for-purpose reasoning rather than familiarity or trend. Scientific integrity: you build models you can explain, defend, and improve - and you apply the same standard to the work of others. Influence through credibility: you earn the confidence of scientists, engineers, and quality professionals by being right, being clear, and being useful - not by title or volume. Bias for impact: you are drawn to problems where the stakes are high and the analytical opportunity is real, and you are energized rather than intimidated by ambiguity. Preferred: 3+ years of hands-on experience designing and building enterprise-grade data pipelines, integration workflows, and data products in complex, multi-source environments. Familiarity with technology transfer workflows, process characterization study design, or commercial process validation (PPQ/PV) in a biologics or pharmaceutical context. Experience in pharmaceutical, biotech, or other regulated life sciences manufacturing environments. Familiarity with GxP data principles, 21 CFR Part 11 compliance, or data integrity requirements in regulated industries. Prior exposure to manufacturing source systems such as MES, process historians (e.g., OSIsoft PI/AVEVA), LIMS, QMS, or ERP platforms Experience building data infrastructure for AI/ML programs - including feature engineering pipelines, model training datasets, or vector/embedding data layers for RAG architectures. Knowledge of biologics manufacturing processes (e.g., upstream cell culture, downstream purification, fill-finish) or CMC development workflows. Familiarity with data mesh, data fabric, or federated data architecture patterns. Experience with graph databases, knowledge graphs, or ontology frameworks applied to scientific or manufacturing data. Contributions to open-source data tooling or demonstrated engagement with the modern data engineering community. Design logical, physical, and conceptual data models using modeling tools (e.g., Erwin, PowerDesigner, dbt). . click apply for full job details
