This job is with eBay, an inclusive employer and a member of myGwork - the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. At eBay, we're more than a global ecommerce leader - we're changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We're committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts. Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work - every day. We're in this together, sustaining the future of our customers, our company, and our planet. Join a team of passionate thinkers, innovators, and dreamers - and help us connect people and build communities to create economic opportunity for all. About the Team and Role: Are you ready to build the infrastructure that powers innovation for a global engineering community? Join the Developer Platform, Productivity & Experience Engineering team within eBay's Global Technology organization! We are the engine behind eBay's engineering force, building and operating an AI-powered Internal Developer Platform (IDP) that includes continuous delivery, enterprise testing, and low-code tools. Our mission is to remove friction from the software lifecycle so engineers can deliver secure, high-quality software safely and easily. In this hands-on role, you will modernize and scale eBay's mission-critical development and staging environments. This is a true builder's role where your solutions will directly elevate the daily experience of our engineering teams, offering a culture of high ownership, continuous learning, and innovative tools! What you will accomplish: Build platform capabilities that improve how eBay engineers develop, test, and deliver software to production. Design scalable backend services and REST APIs using Java or Kotlin within distributed systems. Create intuitive frontend experiences with React that make developer workflows easier to use and more self-service. Build Kubernetes-based capabilities that improve platform reliability while automating common engineering workflows. Reduce friction in the development lifecycle by strengthening shared infrastructure and expanding automation. Work directly with engineers across eBay to understand their needs and shape new platform experiences, including applied AI capabilities. What you will bring: Proven hands-on experience designing and building microservices and distributed systems, ideally utilizing Java and Spring Boot. Demonstrated frontend development experience using React to build seamless, user-friendly internal tools and platforms. Deep technical expertise in Kubernetes and cloud-native infrastructure, with a strong background in operating and scaling complex development environments. Robust Python scripting and automation skills, utilized specifically to enhance system reliability and eliminate manual operational overhead. A high-ownership, builder mindset with strong fundamentals in platform engineering, scalability, and developer productivity. Readiness to work in a collaborative, full-time hybrid environment, participating in standard engineering operational support and team cadences. Additional Details The base pay range for this position is expected in the range below: $98,000 - $173,600 Base pay offered may vary depending on multiple individualized factors, including location, skills, and experience. The total compensation package for this position may also include other elements, including a target bonus and restricted stock units (as applicable) in addition to a full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as PTO and parental leave). Details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employees will be in an "at-will position" and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors. Remote roles are not eligible for U.S. visa sponsorship. eBay is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, veteran status, and disability, or other legally protected status. If you have a need that requires accommodation, please contact us at . We will make every effort to respond to your request for accommodation as soon as possible. View our accessibility statement to learn more about eBay's commitment to ensuring digital accessibility for people with disabilities. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. We use cookies to enhance your experience and may use AI tools for administrative tasks in the hiring process. To learn how we handle your personal data and use AI responsibly, please visit our Talent Privacy Notice , Privacy Center and AI Hiring Guidelines .
11/12/2026
Full time
This job is with eBay, an inclusive employer and a member of myGwork - the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. At eBay, we're more than a global ecommerce leader - we're changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We're committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts. Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work - every day. We're in this together, sustaining the future of our customers, our company, and our planet. Join a team of passionate thinkers, innovators, and dreamers - and help us connect people and build communities to create economic opportunity for all. About the Team and Role: Are you ready to build the infrastructure that powers innovation for a global engineering community? Join the Developer Platform, Productivity & Experience Engineering team within eBay's Global Technology organization! We are the engine behind eBay's engineering force, building and operating an AI-powered Internal Developer Platform (IDP) that includes continuous delivery, enterprise testing, and low-code tools. Our mission is to remove friction from the software lifecycle so engineers can deliver secure, high-quality software safely and easily. In this hands-on role, you will modernize and scale eBay's mission-critical development and staging environments. This is a true builder's role where your solutions will directly elevate the daily experience of our engineering teams, offering a culture of high ownership, continuous learning, and innovative tools! What you will accomplish: Build platform capabilities that improve how eBay engineers develop, test, and deliver software to production. Design scalable backend services and REST APIs using Java or Kotlin within distributed systems. Create intuitive frontend experiences with React that make developer workflows easier to use and more self-service. Build Kubernetes-based capabilities that improve platform reliability while automating common engineering workflows. Reduce friction in the development lifecycle by strengthening shared infrastructure and expanding automation. Work directly with engineers across eBay to understand their needs and shape new platform experiences, including applied AI capabilities. What you will bring: Proven hands-on experience designing and building microservices and distributed systems, ideally utilizing Java and Spring Boot. Demonstrated frontend development experience using React to build seamless, user-friendly internal tools and platforms. Deep technical expertise in Kubernetes and cloud-native infrastructure, with a strong background in operating and scaling complex development environments. Robust Python scripting and automation skills, utilized specifically to enhance system reliability and eliminate manual operational overhead. A high-ownership, builder mindset with strong fundamentals in platform engineering, scalability, and developer productivity. Readiness to work in a collaborative, full-time hybrid environment, participating in standard engineering operational support and team cadences. Additional Details The base pay range for this position is expected in the range below: $98,000 - $173,600 Base pay offered may vary depending on multiple individualized factors, including location, skills, and experience. The total compensation package for this position may also include other elements, including a target bonus and restricted stock units (as applicable) in addition to a full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as PTO and parental leave). Details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employees will be in an "at-will position" and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors. Remote roles are not eligible for U.S. visa sponsorship. eBay is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, veteran status, and disability, or other legally protected status. If you have a need that requires accommodation, please contact us at . We will make every effort to respond to your request for accommodation as soon as possible. View our accessibility statement to learn more about eBay's commitment to ensuring digital accessibility for people with disabilities. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. We use cookies to enhance your experience and may use AI tools for administrative tasks in the hiring process. To learn how we handle your personal data and use AI responsibly, please visit our Talent Privacy Notice , Privacy Center and AI Hiring Guidelines .
Job Description Job Description Please Note: To conform with the United States Government Space Technology Export Regulations, the applicant must be a U.S. citizen, lawful permanent resident of the U.S., conditional resident, asylee or refugee (protected individuals as defined by 8 U.S.C. 1324b(a)(3 , or eligible to obtain the required authorizations from the U.S. Department of State. At CesiumAstro , we are developers and pioneers of out-of-the-box communication systems for satellites, UAVs, launch vehicles, and other space and airborne platforms. We take pride in our dynamic and cross-functional work environment, which allows us to learn, develop, and engage across our organization. If you are looking for hands-on, interactive, and autonomous work, CesiumAstro is the place for you. We are actively seeking passionate, collaborative, energetic, and forward-thinking individuals to join our team. We are designing, building, and launching 8 new satellites in the next four years. They are BIG and super capable RF payload rideshare SmallSats. Our launches are already secured. Our in-house test facilities are built. If you want to own critical communications for these LEO communications satellites and then have the chance to iterate and improve your design at a rapidly growing tech company, this is your chance. We're looking for builders, problem-solvers, and system thinkers. As a Senior Communication Systems Engineer , you will own the design, configuration, and operation of the satellite's space-to-ground communication links. You will define how the satellite communicates with the ground in both nominal and off-nominal conditions, ensuring reliable command, telemetry, and fault recovery. The ideal candidate will have a strong background in systems engineering, expertise with both space-based communication payloads and ground terminals, and proven ability to coordinate activities across multidisciplinary engineering teams. JOB DUTIES AND RESPONSIBILITIES Model, analyze and maintain the satellite's link budget, modulation and coding, ensuring reliable communications from space to ground for all RF links. Architect hardware-based crypto solutions for flight hardware, manage pass-throughs and end points for users and operations. Strong understanding of ground station and network operations with the ability to troubleshoot issues with commercial providers and on-site terminals. Define how the radios are used in different mission phases, including contingency operations and emergency recovery. Contribute to the RF link test campaign and work with flight software and ground teams to validate end-to-end communication link performance, from antenna to command center. Optimize low-SWaP communications, mitigate interference, and ensure robust, fail-safe links. WHAT A SUCCESSFUL CANDIDATE LOOKS LIKE You understand how to design, analyze, and operate spaceborne communication links. You can balance power, bandwidth, protocols and noise to ensure reliable space-to-ground connectivity. You can answer "what's the link budget" with thorough analysis, EbN0, EsN0, or effective data rate given a variety of conditions. Extensive and practical experience with crypto-From COMSEC plan development, key management to system trouble shooting you have applied, on the ground experience. You know how comms fit into the broader mission, from flight software integration to ground operations You are comfortable identifying problems and using your expertise to find solutions. You can prototype, test, and iterate quickly, focusing on what's needed to get a mission off the ground. JOB REQUIREMENTS AND MINIMUM QUALIFICATIONS Bachelor's degree in electrical engineering or a related field A minimum of 5 years of experience with emphasis on satellite communications, RF system engineering, or related fields Experience in design, verification, and operation of satellite communication links Strong understanding of link budget analysis, modulation schemes, and ground station interfaces Hands-on experience configuring and testing ground station equipment and space-qualified radios and antennas Practical understanding of how space links fail, why, and how to fix them before they break Ability to define and implement communication system CONOPS for nominal and emergency operations Experience with satellite communication link licensing and regulatory compliance (ITU/FCC) CesiumAstro considers several factors when extending an offer, including but not limited to, the role and associated responsibilities, a candidate's work experience, education/training, and key skills. Full-time employment offers include company stock options and a generous benefits package including health, dental, vision, HSA, FSA, life, disability and retirement plans. CesiumAstro is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected Veteran Status, or any other characteristic protected by applicable federal, state, or local law. Please note: CesiumAstro does not accept unsolicited resumes from contract agencies or search firms. Any unsolicited resumes submitted to our website or to CesiumAstro team members will be considered property of CesiumAstro, and we will not be obligated to pay any referral fees. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
11/12/2026
Full time
Job Description Job Description Please Note: To conform with the United States Government Space Technology Export Regulations, the applicant must be a U.S. citizen, lawful permanent resident of the U.S., conditional resident, asylee or refugee (protected individuals as defined by 8 U.S.C. 1324b(a)(3 , or eligible to obtain the required authorizations from the U.S. Department of State. At CesiumAstro , we are developers and pioneers of out-of-the-box communication systems for satellites, UAVs, launch vehicles, and other space and airborne platforms. We take pride in our dynamic and cross-functional work environment, which allows us to learn, develop, and engage across our organization. If you are looking for hands-on, interactive, and autonomous work, CesiumAstro is the place for you. We are actively seeking passionate, collaborative, energetic, and forward-thinking individuals to join our team. We are designing, building, and launching 8 new satellites in the next four years. They are BIG and super capable RF payload rideshare SmallSats. Our launches are already secured. Our in-house test facilities are built. If you want to own critical communications for these LEO communications satellites and then have the chance to iterate and improve your design at a rapidly growing tech company, this is your chance. We're looking for builders, problem-solvers, and system thinkers. As a Senior Communication Systems Engineer , you will own the design, configuration, and operation of the satellite's space-to-ground communication links. You will define how the satellite communicates with the ground in both nominal and off-nominal conditions, ensuring reliable command, telemetry, and fault recovery. The ideal candidate will have a strong background in systems engineering, expertise with both space-based communication payloads and ground terminals, and proven ability to coordinate activities across multidisciplinary engineering teams. JOB DUTIES AND RESPONSIBILITIES Model, analyze and maintain the satellite's link budget, modulation and coding, ensuring reliable communications from space to ground for all RF links. Architect hardware-based crypto solutions for flight hardware, manage pass-throughs and end points for users and operations. Strong understanding of ground station and network operations with the ability to troubleshoot issues with commercial providers and on-site terminals. Define how the radios are used in different mission phases, including contingency operations and emergency recovery. Contribute to the RF link test campaign and work with flight software and ground teams to validate end-to-end communication link performance, from antenna to command center. Optimize low-SWaP communications, mitigate interference, and ensure robust, fail-safe links. WHAT A SUCCESSFUL CANDIDATE LOOKS LIKE You understand how to design, analyze, and operate spaceborne communication links. You can balance power, bandwidth, protocols and noise to ensure reliable space-to-ground connectivity. You can answer "what's the link budget" with thorough analysis, EbN0, EsN0, or effective data rate given a variety of conditions. Extensive and practical experience with crypto-From COMSEC plan development, key management to system trouble shooting you have applied, on the ground experience. You know how comms fit into the broader mission, from flight software integration to ground operations You are comfortable identifying problems and using your expertise to find solutions. You can prototype, test, and iterate quickly, focusing on what's needed to get a mission off the ground. JOB REQUIREMENTS AND MINIMUM QUALIFICATIONS Bachelor's degree in electrical engineering or a related field A minimum of 5 years of experience with emphasis on satellite communications, RF system engineering, or related fields Experience in design, verification, and operation of satellite communication links Strong understanding of link budget analysis, modulation schemes, and ground station interfaces Hands-on experience configuring and testing ground station equipment and space-qualified radios and antennas Practical understanding of how space links fail, why, and how to fix them before they break Ability to define and implement communication system CONOPS for nominal and emergency operations Experience with satellite communication link licensing and regulatory compliance (ITU/FCC) CesiumAstro considers several factors when extending an offer, including but not limited to, the role and associated responsibilities, a candidate's work experience, education/training, and key skills. Full-time employment offers include company stock options and a generous benefits package including health, dental, vision, HSA, FSA, life, disability and retirement plans. CesiumAstro is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected Veteran Status, or any other characteristic protected by applicable federal, state, or local law. Please note: CesiumAstro does not accept unsolicited resumes from contract agencies or search firms. Any unsolicited resumes submitted to our website or to CesiumAstro team members will be considered property of CesiumAstro, and we will not be obligated to pay any referral fees. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Job Description Job Description The Role Spear AI is hiring a Senior Infrastructure Engineer to own deployment and authorization on our developer cloud for the undersea enterprise. It's an accredited platform for building, deploying, and operating mission software across unclassified and classified environments. You'll take commercial and partner software from first onboarding through hardened deployment and Authority to Operate, working directly with government security staff to compress timelines without cutting corners. Authorization speed is what we're measured on. This is a builder's role. You decide when to build, when to reuse, and when a problem doesn't need solving, and you leave behind tooling that makes the next deployment faster than the last. The result is real capability in mission users' hands in months, not years. About Spear AI Spear AI is a growing defense contracting company dedicated to delivering cutting-edge solutions that support our nation's security. As we expand, we're building a culture where innovation meets mission-critical work. We operate with a flat organizational structure that empowers every team member to make an impact, collaborate directly with leadership, and contribute to projects that matter. Whether you're joining our Hardware, Software, or Services division, you'll work alongside talented professionals who are committed to excellence and advancing the capabilities that keep our nation safe and secure. Spear AI builds sonobuoy sensors that are deployed into the water and collect edge data. You'll have an opportunity to work on real-world projects that directly impact warfighter capabilities and mission success. The Services Team. You'll sit with Spear AI's infrastructure engineers inside Services, our applied engineering and technical advisory arm. We embed with government customers to design, build, and operate platforms, pipelines, and software that take emerging capability from concept to fielded mission impact. What You'll Do • Design, build, and operate platform and tenant infrastructure across AWS GovCloud and classified cloud environments, defined entirely as code and treated as software: versioned, reviewed, and tested. • Onboard commercial and third-party software: containerize or package it, integrate it with platform identity (CAC/PIV, OIDC, SAML), logging, vulnerability scanning, and artifact services, and get it production-ready. • Drive each capability through the Risk Management Framework to ATO: define the authorization boundary, map inherited and system-specific controls, own the SSP and supporting artifacts, remediate findings, and manage POA&Ms. • Make and defend build, reuse, or buy decisions, with a bias toward the simplest thing that meets the security requirement. • Harden systems to DISA STIGs and DoD/IC baselines and run and respond to vulnerability scans and continuous monitoring. • Build CI/CD and DevSecOps pipelines that work inside air-gapped and restricted networks, including internal registries, mirrored package repositories, and controlled egress. • Serve as the technical point of contact for government ISSMs, ISSOs, assessors, and Authorizing Official staff. • Build the templates, tooling, and golden paths that make every new capability deploy and authorize faster than the last, and mentor less experienced engineers on secure deployment. What We're Looking For A builder, not a console operator. You've designed and run production platforms that other engineers depend on, and you can explain why you built what you built and what you chose not to build. That conversation matters more to us than a list of AWS services on a resume. Infrastructure-as-code repos, internal tooling, and deployment systems you created are what make a candidate stand out. Years are a guide, not a gate. If you've spent four to six years doing serious platform work at a defense tech company or fast-moving startup, we want to talk to you. Accreditation experience is a strong plus but not required. We'll pair you with Spear's accreditation support. Required Skills • Active TS/SCI required, with the ability to obtain a CI Polygraph. Active IC CI Poly is a plus. • DoD 8140 baseline certification (Security+ or higher) is required for privileged access; if you don't have it yet, you'll need to earn it shortly after starting. • 5+ years of hands-on platform, DevOps, or infrastructure engineering on production systems • Real code in Python, Go, or TypeScript, with version control and tests • Infrastructure as Code with the same discipline (Terraform, Pulumi, or similar) • CI/CD pipeline design • Deep working knowledge of containers and Kubernetes, including running them securely in restricted environments • Designing and operating AWS infrastructure: networking, IAM, and encryption boundaries (GovCloud is a plus) • Linux and networking fundamentals (DNS, TLS/PKI, load balancing, routing) and the ability to debug across all of them • Working knowledge of RMF, NIST SP 800-53, DISA STIGs, and vulnerability management • Translating security requirements into technical implementation and explaining it clearly to government security staff • Picking up new AI platforms and integrations quickly, including agentic AI tooling Preferred • Taking a system through RMF to ATO, IATT, or IATC in a DoD or IC environment, including eMASS or Xacta • Deploying commercial software into government-controlled cloud accounts (BYOC, multi-tenant, SCCA-compliant or broker-provisioned structures) • Air-gapped or cross-domain environments: mirrored repositories, controlled egress, cross domain solution integrations • Identity federation (Keycloak, Authentik, or similar) with CAC/PIV, OIDC, and SAML • DevSecOps tooling: SAST/DAST, container image scanning, SBOM generation, policy-as-code • Supporting AI/LLM workloads in government cloud, such as model serving in GovCloud or GPU-backed inference • Military or Intelligence Community experience Nice to haves • IaC & config: Terraform, Pulumi, CloudFormation, Ansible • Identity: Keycloak, Authentik, AWS IAM Identity Center • Security & compliance: eMASS, Xacta, ACAS/Nessus, SCAP/OpenSCAP, Trivy, Grype, SonarQube • DevSecOps & artifacts: GitLab CI/CD, GitHub Enterprise, Artifactory, Nexus, Harbor • Mission platforms as tenants: Palantir (Foundry, AIP), Anduril Lattice • AI serving & Agentic: Amazon Bedrock, vLLM, MCP servers, LangGraph, LiteLLM What we offer Unlimited PTO - Take the time you need to recharge and maintain work-life balance. Dedicated Sick Time - Your health and well-being come first. Comprehensive Health & Benefits - Medical, dental, and vision coverage to keep you and your family protected. 11 Paid Holidays - Enjoy time off throughout the year to celebrate and spend time with loved ones. Professional Development - Educational opportunities and resources to help you grow your skills and advance your career. Collaborative Environment - Work directly with leadership in our flat organizational structure, where your ideas and contributions matter. Mission-Driven Work - Contribute to projects that directly support national security and make a real-world impact. Growth Opportunities - Join us during an exciting expansion phase where you can help shape our future. Additional benefit opportunities when you choose Spear AI 401(k) with company match. Onsite / Remote / Flexible work arrangements or hybrid options (position dependent). Relocation assistance (position dependent). Referral bonuses. Performance bonuses. Life insurance and disability coverage. Technology home office setup stipend. Professional certification reimbursement (position dependent). Compensation We offer competitive compensation tailored to your experience, location, and the impact you'll make. We're committed to equitable pay and will share a range aligned to your level and geography during the hiring process. In accordance with state law, candidates in jurisdictions such as CA, CO, WA, NY, and others, where applicable, will be provided a good-faith salary range upon request and throughout the hiring process. This is a full-time, exempt position under the Fair Labor Standards Act (FLSA) and is not eligible for overtime pay. Compensation for this position is provided on a salaried basis and is not subject to reduction based on hours worked. At Spear AI, you'll find more than just a job; you'll join a mission-driven team where your work directly contributes to national security. Our flat organizational structure means your voice matters, your ideas reach leadership, and your impact is visible. As we grow, we're committed to building robust processes and infrastructure that support both our mission and our people. We value collaboration, continuous improvement, and the expertise each team member brings to the table. If you're looking for a place to grow professionally while working on projects that truly matter, we'd love to hear from you. Equal Opportunity Spear AI is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. • Health insurance benefits • Equity grant for every new employee • Unlimited PTO and a supportive, low-ego environment . click apply for full job details
11/12/2026
Full time
Job Description Job Description The Role Spear AI is hiring a Senior Infrastructure Engineer to own deployment and authorization on our developer cloud for the undersea enterprise. It's an accredited platform for building, deploying, and operating mission software across unclassified and classified environments. You'll take commercial and partner software from first onboarding through hardened deployment and Authority to Operate, working directly with government security staff to compress timelines without cutting corners. Authorization speed is what we're measured on. This is a builder's role. You decide when to build, when to reuse, and when a problem doesn't need solving, and you leave behind tooling that makes the next deployment faster than the last. The result is real capability in mission users' hands in months, not years. About Spear AI Spear AI is a growing defense contracting company dedicated to delivering cutting-edge solutions that support our nation's security. As we expand, we're building a culture where innovation meets mission-critical work. We operate with a flat organizational structure that empowers every team member to make an impact, collaborate directly with leadership, and contribute to projects that matter. Whether you're joining our Hardware, Software, or Services division, you'll work alongside talented professionals who are committed to excellence and advancing the capabilities that keep our nation safe and secure. Spear AI builds sonobuoy sensors that are deployed into the water and collect edge data. You'll have an opportunity to work on real-world projects that directly impact warfighter capabilities and mission success. The Services Team. You'll sit with Spear AI's infrastructure engineers inside Services, our applied engineering and technical advisory arm. We embed with government customers to design, build, and operate platforms, pipelines, and software that take emerging capability from concept to fielded mission impact. What You'll Do • Design, build, and operate platform and tenant infrastructure across AWS GovCloud and classified cloud environments, defined entirely as code and treated as software: versioned, reviewed, and tested. • Onboard commercial and third-party software: containerize or package it, integrate it with platform identity (CAC/PIV, OIDC, SAML), logging, vulnerability scanning, and artifact services, and get it production-ready. • Drive each capability through the Risk Management Framework to ATO: define the authorization boundary, map inherited and system-specific controls, own the SSP and supporting artifacts, remediate findings, and manage POA&Ms. • Make and defend build, reuse, or buy decisions, with a bias toward the simplest thing that meets the security requirement. • Harden systems to DISA STIGs and DoD/IC baselines and run and respond to vulnerability scans and continuous monitoring. • Build CI/CD and DevSecOps pipelines that work inside air-gapped and restricted networks, including internal registries, mirrored package repositories, and controlled egress. • Serve as the technical point of contact for government ISSMs, ISSOs, assessors, and Authorizing Official staff. • Build the templates, tooling, and golden paths that make every new capability deploy and authorize faster than the last, and mentor less experienced engineers on secure deployment. What We're Looking For A builder, not a console operator. You've designed and run production platforms that other engineers depend on, and you can explain why you built what you built and what you chose not to build. That conversation matters more to us than a list of AWS services on a resume. Infrastructure-as-code repos, internal tooling, and deployment systems you created are what make a candidate stand out. Years are a guide, not a gate. If you've spent four to six years doing serious platform work at a defense tech company or fast-moving startup, we want to talk to you. Accreditation experience is a strong plus but not required. We'll pair you with Spear's accreditation support. Required Skills • Active TS/SCI required, with the ability to obtain a CI Polygraph. Active IC CI Poly is a plus. • DoD 8140 baseline certification (Security+ or higher) is required for privileged access; if you don't have it yet, you'll need to earn it shortly after starting. • 5+ years of hands-on platform, DevOps, or infrastructure engineering on production systems • Real code in Python, Go, or TypeScript, with version control and tests • Infrastructure as Code with the same discipline (Terraform, Pulumi, or similar) • CI/CD pipeline design • Deep working knowledge of containers and Kubernetes, including running them securely in restricted environments • Designing and operating AWS infrastructure: networking, IAM, and encryption boundaries (GovCloud is a plus) • Linux and networking fundamentals (DNS, TLS/PKI, load balancing, routing) and the ability to debug across all of them • Working knowledge of RMF, NIST SP 800-53, DISA STIGs, and vulnerability management • Translating security requirements into technical implementation and explaining it clearly to government security staff • Picking up new AI platforms and integrations quickly, including agentic AI tooling Preferred • Taking a system through RMF to ATO, IATT, or IATC in a DoD or IC environment, including eMASS or Xacta • Deploying commercial software into government-controlled cloud accounts (BYOC, multi-tenant, SCCA-compliant or broker-provisioned structures) • Air-gapped or cross-domain environments: mirrored repositories, controlled egress, cross domain solution integrations • Identity federation (Keycloak, Authentik, or similar) with CAC/PIV, OIDC, and SAML • DevSecOps tooling: SAST/DAST, container image scanning, SBOM generation, policy-as-code • Supporting AI/LLM workloads in government cloud, such as model serving in GovCloud or GPU-backed inference • Military or Intelligence Community experience Nice to haves • IaC & config: Terraform, Pulumi, CloudFormation, Ansible • Identity: Keycloak, Authentik, AWS IAM Identity Center • Security & compliance: eMASS, Xacta, ACAS/Nessus, SCAP/OpenSCAP, Trivy, Grype, SonarQube • DevSecOps & artifacts: GitLab CI/CD, GitHub Enterprise, Artifactory, Nexus, Harbor • Mission platforms as tenants: Palantir (Foundry, AIP), Anduril Lattice • AI serving & Agentic: Amazon Bedrock, vLLM, MCP servers, LangGraph, LiteLLM What we offer Unlimited PTO - Take the time you need to recharge and maintain work-life balance. Dedicated Sick Time - Your health and well-being come first. Comprehensive Health & Benefits - Medical, dental, and vision coverage to keep you and your family protected. 11 Paid Holidays - Enjoy time off throughout the year to celebrate and spend time with loved ones. Professional Development - Educational opportunities and resources to help you grow your skills and advance your career. Collaborative Environment - Work directly with leadership in our flat organizational structure, where your ideas and contributions matter. Mission-Driven Work - Contribute to projects that directly support national security and make a real-world impact. Growth Opportunities - Join us during an exciting expansion phase where you can help shape our future. Additional benefit opportunities when you choose Spear AI 401(k) with company match. Onsite / Remote / Flexible work arrangements or hybrid options (position dependent). Relocation assistance (position dependent). Referral bonuses. Performance bonuses. Life insurance and disability coverage. Technology home office setup stipend. Professional certification reimbursement (position dependent). Compensation We offer competitive compensation tailored to your experience, location, and the impact you'll make. We're committed to equitable pay and will share a range aligned to your level and geography during the hiring process. In accordance with state law, candidates in jurisdictions such as CA, CO, WA, NY, and others, where applicable, will be provided a good-faith salary range upon request and throughout the hiring process. This is a full-time, exempt position under the Fair Labor Standards Act (FLSA) and is not eligible for overtime pay. Compensation for this position is provided on a salaried basis and is not subject to reduction based on hours worked. At Spear AI, you'll find more than just a job; you'll join a mission-driven team where your work directly contributes to national security. Our flat organizational structure means your voice matters, your ideas reach leadership, and your impact is visible. As we grow, we're committed to building robust processes and infrastructure that support both our mission and our people. We value collaboration, continuous improvement, and the expertise each team member brings to the table. If you're looking for a place to grow professionally while working on projects that truly matter, we'd love to hear from you. Equal Opportunity Spear AI is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. • Health insurance benefits • Equity grant for every new employee • Unlimited PTO and a supportive, low-ego environment . click apply for full job details
This job is with Braze, an inclusive employer and a member of myGwork - the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. At Braze, we have found our people. We're a genuinely approachable, exceptionally kind, and intensely passionate crew. We seek to ignite that passion by setting high standards, championing teamwork, and creating work-life harmony as we collectively navigate rapid growth on a global scale while striving for greater equity and opportunity - inside and outside our organization. To flourish here, you must be prepared to set a high bar for yourself and those around you. There is always a way to contribute: Acting with autonomy, having accountability and being open to new perspectives are essential to our continued success. Our deep curiosity to learn and our eagerness to share diverse passions with others gives us balance and injects a one-of-a-kind vibrancy into our culture. If you are driven to solve exhilarating challenges and have a bias toward action in the face of change, you will be empowered to make a real impact here, with a sharp and passionate team at your back. If Braze sounds like a place where you can thrive, we can't wait to meet you. About the Role Braze is making AI the default way of working across the company. Our Applied AI Architects are forward-deployed practitioners who combine domain judgment with hands-on build capability: they embed with business teams, treat employees as users and workflows as product surfaces, and ship working agents on shared infrastructure. The function runs in two tracks. GTM Architects are permanently embedded in the GTM organization, each owning a portion of a customer and prospect lifecycle that spans Marketing, Sales, Customer Success, and Support; when they address friction in one stage, they work the surrounding levers holistically, mapping how those functions connect today and how AI could connect them better. G&A Architects rotate across corporate functions like Finance, People, Legal, and Operations, driving each cohort to self-sufficiency. You will lead this team. That means defining how a new role category operates, owning the portfolio of engagements across the business, and answering for the outcomes: adoption that persists, workflows that measurably improve, and committed bets that read out in business metrics like sales ramp time, quota capacity, and competitive win rate. This is a player-coach role. You will carry enough build and field surface yourself to keep your judgment current, while your primary output is the team: who they are, where they embed, what they ship, and whether it works. You inherit the hiring manager seat for all Architect roles, a unified interview loop, and an operating model with real momentum. What the function becomes from there is yours to shape, in close partnership with our Director of Applied AI Product, the CIO and CTO organizations, and the leaders of the functions your team serves. What You'll Do Build the team. Close the open Architect reqs across both tracks (GTM and G&A), then own onboarding, development, and performance for a team of senior, highly autonomous ICs operating in a role category that barely exists in the market. Define what good looks like and what the career path is. Own the engagement portfolio. Decide where Architects embed, what they prioritize, and when they rotate or scale back. Balance permanent GTM surface ownership against rotational G&A engagements. Say no to good ideas so the team ships great ones. Answer for the bets. Own delivery and measurement of the committed GTM bets (sales ramp time, quota capacity / customer-facing time, competitive win rate) and run the promotion pipeline for G&A candidate bets. Every bet pairs a business outcome with leading indicators; you keep both honest. Run the operating discipline. Enforce the engagement lifecycle (discovery, prioritization, readout, design, build, deliver, monitor) and the cohort maturity model. Adoption is the bar: an ignored output is a bug, and your team operates what it ships until it is productized. Stay hands-on. Review agent designs, specs, and output quality at the system level. Carry a build surface of your own. Your field and build credibility is what earns the team its seat in pipeline reviews and functional staff meetings. Compound through the platform. Partner with the Director of Applied AI Product to move field-proven patterns into shared infrastructure, and to feed field reality back into platform requirements, evaluation criteria, and measurement frameworks. Be the executive interface. Negotiate embed agreements with CRO and CBO leadership and functional executives, represent the function's results and roadmap to senior leadership, and protect your team's focus when demand outstrips capacity, which it will. Write the playbook. The role category is being defined in real time across the industry. Document what works, publish it internally, and make Braze a reference point for how a scaled SaaS company runs applied AI. Who You Are A proven leader of senior technical ICs. You have built and led high-autonomy teams: forward-deployed engineering, solutions consulting or architecture, field engineering, or an applied AI/ML group. You know how to manage outcomes without hovering, and how to coach people more specialized than you. A builder with current hands. You have personally built AI agents, automations, or LLM-powered tools that changed how real work gets done, and you can evaluate someone else's system design, retrieval logic, and output quality without an interpreter. Credible in the field. You have spent real time in or alongside the commercial motion of a B2B SaaS company. You can sit in a pipeline review, an EBR, or a functional leadership meeting and have the room take you seriously. A portfolio operator. You think in terms of impact, feasibility, and sequencing across many competing demands, and you can defend your prioritization to executives who wanted a different answer. Fluent in measurement. You can design a baseline, argue a leading indicator, dollarize an efficiency gain, and tell the difference between a metric that proves a mechanism and one that flatters a narrative. A zero-to-one leader. You have stood up a function, practice, or team where no playbook existed, and you are comfortable being accountable for results while the ground is still moving. Minimum Requirements 10+ years of professional experience spanning GTM-adjacent technical work (solutions consulting/architecture, field or forward-deployed engineering, technical GTM strategy) and hands-on building 3+ years leading senior individual contributors, with evidence you developed and retained them Demonstrated, hands-on experience building AI-powered tools, agents, or workflows that transformed real work, with concrete examples you can speak to in depth Track record of standing up a new team, practice, or function and scaling it past your own personal capacity Executive-level communication and stakeholder management, including negotiating priorities with senior leaders Experience defining and defending business-impact measurement for a technical team Bonus Points You have led forward-deployed engineering or applied AI teams at a model lab, an AI-native company, or a scaled enterprise SaaS business You have shipped with modern agentic tooling (Claude, Claude Code, Cursor, custom agent frameworks, MCP-style integrations) and enterprise GTM stacks (Salesforce, Gong, Slack, Snowflake) You have run engagement- or rotation-based operating models (consulting, embedded product teams, customer zero programs) You have hired for hard-to-define roles and built interview systems that actually predicted performance Why This Role Matters The judgment of a company's best practitioners has always been its scarcest asset. The technology now exists to encode that judgment into systems that deliver it at scale, and Braze has committed the budget, the executive backing, and the operating model to do it. What the function needs now is the leader who turns a funded plan and a strong first hire into a team the rest of the company builds its expectations around. The window to define this role category, inside Braze and across the industry, is open right now. For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $193,000 and $276,000/year, with an expected On Target Earnings (OTE) between $241,000 and $345,000/year (including bonus or commission).Your exact offer may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company. WHAT WE OFFER Braze benefits vary by location, and we encourage you to review our specific benefits offerings for each country at here . More details on benefits plans will be provided if you receive an offer of employment. From offering comprehensive benefits to fostering hybrid ways of working, we've got you covered so you can prioritize work-life harmony. Braze offers benefits such as: Competitive compensation that may include equity . click apply for full job details
11/07/2026
Full time
This job is with Braze, an inclusive employer and a member of myGwork - the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. At Braze, we have found our people. We're a genuinely approachable, exceptionally kind, and intensely passionate crew. We seek to ignite that passion by setting high standards, championing teamwork, and creating work-life harmony as we collectively navigate rapid growth on a global scale while striving for greater equity and opportunity - inside and outside our organization. To flourish here, you must be prepared to set a high bar for yourself and those around you. There is always a way to contribute: Acting with autonomy, having accountability and being open to new perspectives are essential to our continued success. Our deep curiosity to learn and our eagerness to share diverse passions with others gives us balance and injects a one-of-a-kind vibrancy into our culture. If you are driven to solve exhilarating challenges and have a bias toward action in the face of change, you will be empowered to make a real impact here, with a sharp and passionate team at your back. If Braze sounds like a place where you can thrive, we can't wait to meet you. About the Role Braze is making AI the default way of working across the company. Our Applied AI Architects are forward-deployed practitioners who combine domain judgment with hands-on build capability: they embed with business teams, treat employees as users and workflows as product surfaces, and ship working agents on shared infrastructure. The function runs in two tracks. GTM Architects are permanently embedded in the GTM organization, each owning a portion of a customer and prospect lifecycle that spans Marketing, Sales, Customer Success, and Support; when they address friction in one stage, they work the surrounding levers holistically, mapping how those functions connect today and how AI could connect them better. G&A Architects rotate across corporate functions like Finance, People, Legal, and Operations, driving each cohort to self-sufficiency. You will lead this team. That means defining how a new role category operates, owning the portfolio of engagements across the business, and answering for the outcomes: adoption that persists, workflows that measurably improve, and committed bets that read out in business metrics like sales ramp time, quota capacity, and competitive win rate. This is a player-coach role. You will carry enough build and field surface yourself to keep your judgment current, while your primary output is the team: who they are, where they embed, what they ship, and whether it works. You inherit the hiring manager seat for all Architect roles, a unified interview loop, and an operating model with real momentum. What the function becomes from there is yours to shape, in close partnership with our Director of Applied AI Product, the CIO and CTO organizations, and the leaders of the functions your team serves. What You'll Do Build the team. Close the open Architect reqs across both tracks (GTM and G&A), then own onboarding, development, and performance for a team of senior, highly autonomous ICs operating in a role category that barely exists in the market. Define what good looks like and what the career path is. Own the engagement portfolio. Decide where Architects embed, what they prioritize, and when they rotate or scale back. Balance permanent GTM surface ownership against rotational G&A engagements. Say no to good ideas so the team ships great ones. Answer for the bets. Own delivery and measurement of the committed GTM bets (sales ramp time, quota capacity / customer-facing time, competitive win rate) and run the promotion pipeline for G&A candidate bets. Every bet pairs a business outcome with leading indicators; you keep both honest. Run the operating discipline. Enforce the engagement lifecycle (discovery, prioritization, readout, design, build, deliver, monitor) and the cohort maturity model. Adoption is the bar: an ignored output is a bug, and your team operates what it ships until it is productized. Stay hands-on. Review agent designs, specs, and output quality at the system level. Carry a build surface of your own. Your field and build credibility is what earns the team its seat in pipeline reviews and functional staff meetings. Compound through the platform. Partner with the Director of Applied AI Product to move field-proven patterns into shared infrastructure, and to feed field reality back into platform requirements, evaluation criteria, and measurement frameworks. Be the executive interface. Negotiate embed agreements with CRO and CBO leadership and functional executives, represent the function's results and roadmap to senior leadership, and protect your team's focus when demand outstrips capacity, which it will. Write the playbook. The role category is being defined in real time across the industry. Document what works, publish it internally, and make Braze a reference point for how a scaled SaaS company runs applied AI. Who You Are A proven leader of senior technical ICs. You have built and led high-autonomy teams: forward-deployed engineering, solutions consulting or architecture, field engineering, or an applied AI/ML group. You know how to manage outcomes without hovering, and how to coach people more specialized than you. A builder with current hands. You have personally built AI agents, automations, or LLM-powered tools that changed how real work gets done, and you can evaluate someone else's system design, retrieval logic, and output quality without an interpreter. Credible in the field. You have spent real time in or alongside the commercial motion of a B2B SaaS company. You can sit in a pipeline review, an EBR, or a functional leadership meeting and have the room take you seriously. A portfolio operator. You think in terms of impact, feasibility, and sequencing across many competing demands, and you can defend your prioritization to executives who wanted a different answer. Fluent in measurement. You can design a baseline, argue a leading indicator, dollarize an efficiency gain, and tell the difference between a metric that proves a mechanism and one that flatters a narrative. A zero-to-one leader. You have stood up a function, practice, or team where no playbook existed, and you are comfortable being accountable for results while the ground is still moving. Minimum Requirements 10+ years of professional experience spanning GTM-adjacent technical work (solutions consulting/architecture, field or forward-deployed engineering, technical GTM strategy) and hands-on building 3+ years leading senior individual contributors, with evidence you developed and retained them Demonstrated, hands-on experience building AI-powered tools, agents, or workflows that transformed real work, with concrete examples you can speak to in depth Track record of standing up a new team, practice, or function and scaling it past your own personal capacity Executive-level communication and stakeholder management, including negotiating priorities with senior leaders Experience defining and defending business-impact measurement for a technical team Bonus Points You have led forward-deployed engineering or applied AI teams at a model lab, an AI-native company, or a scaled enterprise SaaS business You have shipped with modern agentic tooling (Claude, Claude Code, Cursor, custom agent frameworks, MCP-style integrations) and enterprise GTM stacks (Salesforce, Gong, Slack, Snowflake) You have run engagement- or rotation-based operating models (consulting, embedded product teams, customer zero programs) You have hired for hard-to-define roles and built interview systems that actually predicted performance Why This Role Matters The judgment of a company's best practitioners has always been its scarcest asset. The technology now exists to encode that judgment into systems that deliver it at scale, and Braze has committed the budget, the executive backing, and the operating model to do it. What the function needs now is the leader who turns a funded plan and a strong first hire into a team the rest of the company builds its expectations around. The window to define this role category, inside Braze and across the industry, is open right now. For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $193,000 and $276,000/year, with an expected On Target Earnings (OTE) between $241,000 and $345,000/year (including bonus or commission).Your exact offer may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company. WHAT WE OFFER Braze benefits vary by location, and we encourage you to review our specific benefits offerings for each country at here . More details on benefits plans will be provided if you receive an offer of employment. From offering comprehensive benefits to fostering hybrid ways of working, we've got you covered so you can prioritize work-life harmony. Braze offers benefits such as: Competitive compensation that may include equity . click apply for full job details
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
09/30/2026
Full time
Job Description Job Description Hi, We're AppFolio We're innovators, changemakers, and collaborators. We're more than just a software company - we're building the AI-native platform where the real estate industry comes to do business. We're transforming property management: how properties are leased, how residents find their homes, and how intelligence flows across an entire portfolio. Realm-X is AppFolio's AI-native platform powering this transformation. Within it, Realm-X Leasing Performer is an autonomous AI agent that handles the end-to-end leasing lifecycle - lead management, tour scheduling, follow-up, application processing, etc. - on behalf of property managers and leasing teams. It's one of AppFolio's most ambitious bets on autonomous AI, and it needs ML engineering worthy of that ambition. Who We Are Looking For We're hiring a Staff Machine Learning Engineer to own the ML strategy and execution that makes the Realm-X Leasing Performer production-grade, observable, and continuously improving. You'll sit at the intersection of applied ML, agent systems, and leasing domain expertise - working directly with Leasing Engineering, Voice & Agents, and Research ML to translate prototypes into systems our customers can depend on every day. This isn't a platform-only role. You'll be close enough to the product to shape how the Leasing Performer reasons, acts, and learns - and close enough to infrastructure to make sure it's reliable, cost-efficient, and safe at scale. Your Impact Own the ML Strategy for Leasing: Define and drive the machine learning roadmap across Leasing products - identifying where ML creates the most leverage, making the right model and architecture bets, and working closely with Product and Engineering leadership to align the team around a coherent technical vision that reflects real customer outcomes. Drive the Development & Architecture for Autonomous AI Agents: Be the ML lead for AppFolio's autonomous leasing agent - shaping how it communicates with prospective tenants and helps streamline leasing operations. You'll own the model quality, evaluation framework, and continuous improvement loop that makes the Performer better over time. Translate Research into Product: Partner with Voice & Agents and Research ML to evaluate new capabilities - fine-tuning approaches, retrieval strategies, agentic patterns - and make the call on what's ready to ship and what needs more hardening before it reaches customers. Drive Model Quality and Evaluation: Build the evaluation and experimentation infrastructure that lets the Leasing team ship ML changes with confidence - defining what "better" looks like for leasing-specific tasks and owning the metrics that reflect real customer outcomes. Set the ML Bar for Leasing Engineering: Establish the patterns, standards, and practices that the broader Leasing Engineering team follows when integrating ML - from prompt engineering and RAG to fine-tuning and model selection. Be the person the team comes to when the ML question is hard. Operate with Production Discipline: Ensure that ML systems powering the Leasing Performer meet the reliability bar that production SaaS demands - SLOs, observability, cost discipline, and a clear on-call posture. You don't have to build all of it, but you own the outcomes. Qualifications Systems thinker: You think in terms of platforms and long-term leverage, not just features. You understand how ML infrastructure decisions compound over time. Production builder: You've built and scaled ML infrastructure in production with meaningful business impact - and you treat it like any other production system. Domain curiosity: You take time to understand the business workflows your systems serve - in this case, leasing - and use that understanding to make better technical bets. Ambiguity: You operate effectively in high ambiguity, turning unclear infra problems into clear direction. Owner-operator: You take ownership with a founder mindset, act with urgency, and focus on outcomes. Collaboration: You are humble, collaborative, and low-ego - you elevate those around you and work fluidly across ML, product, and engineering. Reliability mindset: You treat ML infra like any other production system: SLOs, on-call, observability, postmortems. Sustainability: You value work-life balance as a foundation for sustained high performance. Must Have ML Development at scale: Has built and supported production ML systems at scale. Architectural Leadership: You have experience leading architectural discussions, defining system design, and guiding technical decision-making. Inference & Training: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. Training capability: Has trained or fine-tuned language models end-to-end; comfortable with deep learning, evaluation, and inference. RAG & agents: Hands-on experience with LangChain / LangGraph and modern RAG patterns over structured and unstructured data. AI safety & authorization: Hands-on experience operating AI guardrails, scoped tool permissions, and authorization layers for production AI systems - especially in agentic contexts. Nice to Have Experience building ML systems for conversational AI, leasing, or CRM-adjacent workflows. GPU performance tuning (vLLM, TensorRT, Triton, or similar). Experience with ontology-driven systems or knowledge graphs supporting AI applications. Familiarity with real estate, property management, or leasing workflows. Contributions to open-source ML infrastructure or LLM tooling. Location Find out more about our locations by visiting our site. All late-stage candidates complete an in-person meeting with an AppFolian as part of our hiring process. Compensation & Benefits The compensation that we reasonably expect to pay for this role is: $200,000 - $275,000 base pay. The actual compensation for this role will be determined by a variety of factors, including but not limited to the candidate's skills, education, experience, and internal equity. Please note that compensation is just one aspect of a comprehensive Total Rewards package. The compensation range listed here does not include additional benefits or any discretionary bonuses you may be eligible for based on your role and/or employment type. Regular full-time employees are eligible for benefits - see here.
MaxIT Consulting - Max Corporate Group
San Francisco, California
Job Description Job Description SF Bay Area, California Fully On-site We are seeking an Founding Computer Vision Engineer - Multimodal AI & VLMs to join an early-stage technology company building AI systems for real-world industrial environments. This role focuses on multimodal visual intelligence running in hardware-constrained environments and supporting complex physical workflows. The Opportunity You will own critical parts of the AI layer, building and deploying vision-language systems that reason over images and video in real customer environments. The role combines applied AI, computer vision, evaluation systems, and production engineering. Key Responsibilities Build and deploy agentic vision-language systems for real-world industrial workflows. Develop multimodal reasoning capabilities across images and video. Work with detection, segmentation, visual reasoning, VLMs, and related applied computer vision techniques. Own model orchestration and production deployment across hardware-constrained environments. Design and maintain evaluation systems including ground-truth datasets, trajectory evaluation, and regression testing. Iterate on production models based on real-world usage and measurable system performance. Required Qualifications Approximately 1 to 5 years of relevant professional experience. Hands-on experience shipping computer vision, multimodal, or vision-language systems to production. Strong practical understanding of visual AI applied to images and/or video. Experience owning technical systems end to end rather than working exclusively on research prototypes. Comfort operating in real-time, edge, hardware-constrained, or connectivity-constrained environments. Strong software engineering fundamentals and a high level of technical ownership. Candidate Profile The strongest candidates are practical builders who have shipped real systems with real users. Experience in robotics, autonomous systems, drones, wearables, real-time video, edge computing, or adjacent physical-world AI domains is highly relevant. Work Arrangement This position is fully in person in the San Francisco Bay Area. The initial working location is in Burlingame, with the team expected to transition to a San Francisco office. Work Authorization Candidates must be able to work in the United States. Certain visa sponsorship, transfer, or change-of-status scenarios may be supported for candidates already located in the United States. New petitions requiring processing from abroad are not supported at this time.
09/26/2026
Full time
Job Description Job Description SF Bay Area, California Fully On-site We are seeking an Founding Computer Vision Engineer - Multimodal AI & VLMs to join an early-stage technology company building AI systems for real-world industrial environments. This role focuses on multimodal visual intelligence running in hardware-constrained environments and supporting complex physical workflows. The Opportunity You will own critical parts of the AI layer, building and deploying vision-language systems that reason over images and video in real customer environments. The role combines applied AI, computer vision, evaluation systems, and production engineering. Key Responsibilities Build and deploy agentic vision-language systems for real-world industrial workflows. Develop multimodal reasoning capabilities across images and video. Work with detection, segmentation, visual reasoning, VLMs, and related applied computer vision techniques. Own model orchestration and production deployment across hardware-constrained environments. Design and maintain evaluation systems including ground-truth datasets, trajectory evaluation, and regression testing. Iterate on production models based on real-world usage and measurable system performance. Required Qualifications Approximately 1 to 5 years of relevant professional experience. Hands-on experience shipping computer vision, multimodal, or vision-language systems to production. Strong practical understanding of visual AI applied to images and/or video. Experience owning technical systems end to end rather than working exclusively on research prototypes. Comfort operating in real-time, edge, hardware-constrained, or connectivity-constrained environments. Strong software engineering fundamentals and a high level of technical ownership. Candidate Profile The strongest candidates are practical builders who have shipped real systems with real users. Experience in robotics, autonomous systems, drones, wearables, real-time video, edge computing, or adjacent physical-world AI domains is highly relevant. Work Arrangement This position is fully in person in the San Francisco Bay Area. The initial working location is in Burlingame, with the team expected to transition to a San Francisco office. Work Authorization Candidates must be able to work in the United States. Certain visa sponsorship, transfer, or change-of-status scenarios may be supported for candidates already located in the United States. New petitions requiring processing from abroad are not supported at this time.
Job Description Job Description FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California's robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field. Who are We? Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications. Learn more at . About the Job Our Field Foundation Model (FFM) powers a global fleet of autonomous robots that capture massive streams of multimodal data across diverse, dynamic environments every day. As part of the Insight Team our mission is to transform this raw, multimodal data into actionable insights that empower our customers and engineers to deliver value. Field-insight Foundation Model (FiFM) is at the core of how we transform multimodal data from autonomous robots into actionable insights. As an AI/ML Engineer on the FiFM team, you will drive research and model development for one of Field AI's most ambitious initiatives. Your work will span computer vision, vision-language models (VLMs), multimodal scene understanding, and long-memory video analysis and search , with a strong emphasis on agentic AI (tool use, memory, multimodal retrieval-augmented generation).This is a full-cycle ML role : you'll curate datasets, fine-tune and evaluate models, optimize inference, and deploy them into production. It's a blend of applied research and engineering , requiring creativity, rapid experimentation, and rigorous problem-solving. While FiFM is your primary focus, you'll also contribute to broader perception and insight-generation initiatives across Field AI. What You'll Get To Do: Train and fine-tune million- to billion-parameter multimodal models , with a focus on computer vision , video understanding , and vision-language integration . Track state-of-the-art research , adapt novel algorithms, and integrate them into FiFM. Curate datasets and develop tools to improve model interpretability . Build scalable evaluation pipelines for vision and multimodal models . Contribute to model observability , drift detection , and error classification . Fine-tune and optimize open-source VLMs and multimodal embedding models for efficiency and robustness. Build and optimize Multi-VectorRAG pipelines with vector DBs and knowledge graphs . Create embedding-based memory and retrieval chains with token-efficient chunking strategies. What You Have: Master's/Ph.D. in Computer Science, AI/ML, Robotics, or equivalent industry experience. 2+ years of industry experience or relevant publications in CV/ML/AI. Strong expertise in computer vision, video understanding, temporal modeling, and VLMs . Proficiency in Python and PyTorch with production-level coding skills. Experience building pipelines for large-scale video/image datasets . Familiarity with AWS or other cloud platforms for ML training and deployment. Understanding of MLOps best practices (CI/CD, experiment tracking). Hands-on experience fine-tuning open-source multimodal models using HuggingFace, DeepSpeed, vLLM, FSDP, LoRA/QLoRA. Knowledge of precision tradeoffs (FP16, bfloat16, quantization) and multi-GPU optimization . Ability to design scalable evaluation pipelines for vision/VLMs and agent performance. The Extras That Set You Apart: Experience with Agentic/RAG pipelines and knowledge graphs (LangChain, LangGraph, LlamaIndex, OpenSearch, FAISS, Pinecone). Familiarity with agent operations logging and evaluation frameworks . Background in optimization : token cost reduction, chunking strategies, reranking, and retrieval latency tuning. Experience deploying models under quantized (int4/int8) and distributed multi-GPU inference . Exposure to open-vocabulary detection, zero/few-shot learning, multimodal RAG . Knowledge of temporal-spatial modeling (event/scene graphs). Experience deploying AI in edge or resource-constrained environments . Our salary range is generous and we consider each individual's background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market. Why Join FieldAI in Irvine? In Irvine, you will work where the robots are. Our local team builds and tests systems on real hardware with real sensors, then ships them to operate in unstructured, previously unknown environments around the world. We are solving one of robotics' hardest challenges: reliable deployment outside the lab. Our Field Foundational Models raise the bar for perception, planning, localization, and manipulation, with an emphasis on explainability and safety for real-world use. You will collaborate with a world-class team that thrives on creativity, resilience, and bold thinking. We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX, along with a track record of field deployments and strong performance in DARPA challenge segments. Be Part of the Next Robotics Revolution We are looking for builders who want their work to leave the whiteboard and show up on robots. If you enjoy tackling tough, uncharted questions and working across disciplines, you will find your people here. Our teams span AI, software, robotics engineering, product, field deployment, and technical communication, all focused on shipping systems that perform in the real world. Our headquarters is in Irvine, and we partner closely with teams there as well as colleagues across the US and around the world. Join us in Southern California and help define what dependable, field-ready autonomy looks like. We value diverse perspectives and are committed to fostering an inclusive workplace. We evaluate candidates and employees based on merit, qualifications, and performance, and we do not discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, or any other legally protected status. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
09/26/2026
Full time
Job Description Job Description FieldAI's Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California's robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field. Who are We? Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications. Learn more at . About the Job Our Field Foundation Model (FFM) powers a global fleet of autonomous robots that capture massive streams of multimodal data across diverse, dynamic environments every day. As part of the Insight Team our mission is to transform this raw, multimodal data into actionable insights that empower our customers and engineers to deliver value. Field-insight Foundation Model (FiFM) is at the core of how we transform multimodal data from autonomous robots into actionable insights. As an AI/ML Engineer on the FiFM team, you will drive research and model development for one of Field AI's most ambitious initiatives. Your work will span computer vision, vision-language models (VLMs), multimodal scene understanding, and long-memory video analysis and search , with a strong emphasis on agentic AI (tool use, memory, multimodal retrieval-augmented generation).This is a full-cycle ML role : you'll curate datasets, fine-tune and evaluate models, optimize inference, and deploy them into production. It's a blend of applied research and engineering , requiring creativity, rapid experimentation, and rigorous problem-solving. While FiFM is your primary focus, you'll also contribute to broader perception and insight-generation initiatives across Field AI. What You'll Get To Do: Train and fine-tune million- to billion-parameter multimodal models , with a focus on computer vision , video understanding , and vision-language integration . Track state-of-the-art research , adapt novel algorithms, and integrate them into FiFM. Curate datasets and develop tools to improve model interpretability . Build scalable evaluation pipelines for vision and multimodal models . Contribute to model observability , drift detection , and error classification . Fine-tune and optimize open-source VLMs and multimodal embedding models for efficiency and robustness. Build and optimize Multi-VectorRAG pipelines with vector DBs and knowledge graphs . Create embedding-based memory and retrieval chains with token-efficient chunking strategies. What You Have: Master's/Ph.D. in Computer Science, AI/ML, Robotics, or equivalent industry experience. 2+ years of industry experience or relevant publications in CV/ML/AI. Strong expertise in computer vision, video understanding, temporal modeling, and VLMs . Proficiency in Python and PyTorch with production-level coding skills. Experience building pipelines for large-scale video/image datasets . Familiarity with AWS or other cloud platforms for ML training and deployment. Understanding of MLOps best practices (CI/CD, experiment tracking). Hands-on experience fine-tuning open-source multimodal models using HuggingFace, DeepSpeed, vLLM, FSDP, LoRA/QLoRA. Knowledge of precision tradeoffs (FP16, bfloat16, quantization) and multi-GPU optimization . Ability to design scalable evaluation pipelines for vision/VLMs and agent performance. The Extras That Set You Apart: Experience with Agentic/RAG pipelines and knowledge graphs (LangChain, LangGraph, LlamaIndex, OpenSearch, FAISS, Pinecone). Familiarity with agent operations logging and evaluation frameworks . Background in optimization : token cost reduction, chunking strategies, reranking, and retrieval latency tuning. Experience deploying models under quantized (int4/int8) and distributed multi-GPU inference . Exposure to open-vocabulary detection, zero/few-shot learning, multimodal RAG . Knowledge of temporal-spatial modeling (event/scene graphs). Experience deploying AI in edge or resource-constrained environments . Our salary range is generous and we consider each individual's background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market. Why Join FieldAI in Irvine? In Irvine, you will work where the robots are. Our local team builds and tests systems on real hardware with real sensors, then ships them to operate in unstructured, previously unknown environments around the world. We are solving one of robotics' hardest challenges: reliable deployment outside the lab. Our Field Foundational Models raise the bar for perception, planning, localization, and manipulation, with an emphasis on explainability and safety for real-world use. You will collaborate with a world-class team that thrives on creativity, resilience, and bold thinking. We bring deep experience from organizations such as DeepMind, NASA JPL, Boston Dynamics, NVIDIA, Amazon, Tesla Autopilot, Cruise, Zoox, Toyota Research Institute, and SpaceX, along with a track record of field deployments and strong performance in DARPA challenge segments. Be Part of the Next Robotics Revolution We are looking for builders who want their work to leave the whiteboard and show up on robots. If you enjoy tackling tough, uncharted questions and working across disciplines, you will find your people here. Our teams span AI, software, robotics engineering, product, field deployment, and technical communication, all focused on shipping systems that perform in the real world. Our headquarters is in Irvine, and we partner closely with teams there as well as colleagues across the US and around the world. Join us in Southern California and help define what dependable, field-ready autonomy looks like. We value diverse perspectives and are committed to fostering an inclusive workplace. We evaluate candidates and employees based on merit, qualifications, and performance, and we do not discriminate on the basis of race, color, gender, national origin, ethnicity, veteran status, disability status, age, sexual orientation, gender identity, marital status, or any other legally protected status. We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
