Software Engineer II, Autonomy Evaluation & Metrics Platform

About Rivian Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract. As a company, we constantly challenge what’s possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations. Role Summary Rivian’s Autonomy Eval Infrastructure team builds the software that helps engineers understand and improve the ML models behind our driving systems and gives release teams evidence they can trust. Within Autonomy’s AI Platform organization, we connect model development with evaluation workflows, metrics, triage and visualization across simulation and real-world vehicle data. As a metrics-platform engineer, you will build backend services, Python libraries and data pipelines that turn model outputs into reproducible measurements and useful comparisons. Working alongside Perception, Planning and Systems engineers, you will learn how models behave, investigate regressions and help turn evaluation findings into better models and data, as well as trusted release scorecards. You will own well-scoped components from implementation through deployment and operation, with support from senior engineers on design and complex trade-offs. Joining this newly formed team offers the opportunity to help shape a shared platform while developing your software engineering and distributed-systems skills. Responsibilities Build and test services, APIs and libraries for registering metric definitions, computing results and retrieving them consistently across engineering tools. Implement scalable metric-processing pipelines with safe retries, incremental computation and backfills. Make missing inputs, failed jobs and incomplete results visible. Capture code, metric, model, dataset and scenario versions so a reported result can be traced to its inputs and reproduced. Build tools for comparing model versions and exploring results by scenario, geography, vehicle platform and operating conditions, using consistent aggregation and denominators. Work with model developers to run evaluation experiments, inspect predictions and investigate failure patterns. Help translate findings into targeted evaluation datasets and regression tests. Integrate metrics into evaluation workflows, CI regression checks and release scorecards, and connect results to recordings and triage tools for investigation. Improve automated tests, performance, observability and deployment tooling. Debug production issues and document the components you support. Work with engineers in Palo Alto, London and Belgrade to turn user needs into maintainable software, participate in design and code reviews, and deliver improvements iteratively. Qualifications 2+ years of software engineering experience, or equivalent demonstrated experience building and maintaining substantial software systems. Strong Python programming skills and a solid foundation in data structures, algorithms, software design and debugging. Practical SQL skills, with AI coding tools expected to support writing and refining queries; ability to validate query logic, results and performance. Experience building backend services, reusable libraries or data-processing pipelines with automated tests, version control and code review. Ability to reason about APIs, data models and component design, explain trade-offs, and identify failure cases such as duplicate, delayed or missing data. Experience with cloud services, containers and CI/CD, and an understanding of how software is deployed and monitored in production. Care with data correctness and basic statistical reasoning, including sample sizes, rates and variability; curiosity about how measurements reflect real driving behavior. Clear written communication and a collaborative approach to gathering requirements, asking for feedback and resolving technical problems. Bonus Points Hands-on experience with ML models using PyTorch or similar frameworks, such as running inference or analyzing prediction errors; experience in autonomy, robotics or simulation. Experience with distributed data processing or analytical storage, such as Spark, Databricks, Delta Lake or comparable systems. Experience with AWS, Docker and GitLab CI, or with developer tools, experiment tracking, data lineage or results visualization. Prior autonomy or robotics experience is welcome but is not required for this role. Pay Disclosure Salary Range for California Based Applicants: $142,000-$177,500 (actual compensation will be determined based on experience, location, and other factors permitted by law). Benefits Summary: Rivian provides robust medical/Rx, dental and vision insurance packages for full-time employees, their spouse or domestic partner, and children up to age 26. Coverage is effective on the first day of employment, and Rivian covers most of the premiums. Equal Opportunity Rivian is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender, gender expression, gender identity, genetic information or characteristics, physical or mental disability, marital/domestic partner status, age, military/veteran status, medical condition, or any other characteristic protected by law. Rivian is committed to ensuring that our hiring process is accessible for persons with disabilities. If you have a disability or limitation, such as those covered by the Americans with Disabilities Act, that requires accommodations to assist you in the search and application process, please email us at candidateaccommodations@rivian.com. Candidate Data Privacy and Technology Rivian may collect, use and disclose your personal information or personal data (within the meaning of the applicable data protection laws) when you apply for employment and/or participate in our recruitment processes (“Candidate Personal Data”). This data includes contact, demographic, communications, educational, professional, employment, social media/website, network/device, recruiting system usage/interaction, security and preference information. Rivian may use your Candidate Personal Data for the purposes of (i) tracking interactions with our recruiting system; (ii) carrying out, analyzing and improving our application and recruitment process, including assessing you and your application and conducting employment, background and reference checks; (iii) establishing an employment relationship or entering into an employment contract with you; (iv) complying with our legal, regulatory and corporate governance obligations; (v) recordkeeping; (vi) ensuring network and information security and preventing fraud; and (vii) as otherwise required or permitted by applicable law. Rivian may share your Candidate Personal Data with (i) internal personnel who have a need to know such information in order to perform their duties, including individuals on our People Team, Finance, Legal, and the team(s) with the position(s) for which you are applying; (ii) Rivian affiliates; and (iii) Rivian’s service providers, including providers of background checks, staffing services, and cloud services. Rivian may transfer or store internationally your Candidate Personal Data, including to or in the United States, Canada, the United Kingdom, and the European Union and in the cloud, and this data may be subject to the laws and accessible to the courts, law enforcement and national security authorities of such jurisdictions. How We Use AI in Our Hiring Process: To ensure transparency, we want candidates to know that Rivian uses iCIMS Talent Cloud Artificial Intelligence (TCAI) and AI-enabled tools to assist with screening, reviewing, organizing and highlighting profiles and applications that match the key requirements for each role. AI does not make hiring decisions: Qualified candidate applications are reviewed by a member of our team, and all decisions throughout the process are made by humans. We use AI to support efficiency and consistency, not to replace human judgment. We are committed to a fair, thoughtful, and equitable experience for every candidate. Participation in AI profile matching is entirely voluntary. If you prefer that your profile not be used in this process, you can opt out at any time. Opting out means your profile will be excluded from automated matching and will not be surfaced for additional roles through this system. Your current application remains active and will not be affected in any way. Interview Integrity & AI Policy Our interview process is designed to evaluate your personal technical foundation, real-time problem-solving logic, and structured capabilities. Therefore, the use of assistive generative AI tools to formulate or deliver your solutions during live interview sessions is not permitted. We care most about how you think - how you make sense of unclear requirements, weigh trade-offs, and explain why you chose one approach over another. We value your unique human perspective, your authenticity, and the journey it takes to reach a solution. Please note that we are currently not accepting applications from third party application services. Build and test services, APIs and libraries for registering metric definitions, computing results and retrieving them consistently across engineering tools. Implement scalable metric-processing pipelines with safe retries, incremental computation and backfills. Make missing inputs, failed jobs and incomplete results visible. Capture code, metric, model, dataset and scenario versions so a reported result can be traced to its inputs and reproduced. Build tools for comparing model versions and exploring results by scenario, geography, vehicle platform and operating conditions, using consistent aggregation and denominators. Work with model developers to run evaluation experiments, inspect predictions and investigate failure patterns. Help translate findings into targeted evaluation datasets and regression tests. Integrate metrics into evaluation workflows, CI regression checks and release scorecards, and connect results to recordings and triage tools for investigation. Improve automated tests, performance, observability and deployment tooling. Debug production issues and document the components you support. Work with engineers in Palo Alto, London and Belgrade to turn user needs into maintainable software, participate in design and code reviews, and deliver improvements iteratively. 2+ years of software engineering experience, or equivalent demonstrated experience building and maintaining substantial software systems. Strong Python programming skills and a solid foundation in data structures, algorithms, software design and debugging. Practical SQL skills, with AI coding tools expected to support writing and refining queries; ability to validate query logic, results and performance. Experience building backend services, reusable libraries or data-processing pipelines with automated tests, version control and code review. Ability to reason about APIs, data models and component design, explain trade-offs, and identify failure cases such as duplicate, delayed or missing data. Experience with cloud services, containers and CI/CD, and an understanding of how software is deployed and monitored in production. Care with data correctness and basic statistical reasoning, including sample sizes, rates and variability; curiosity about how measurements reflect real driving behavior. Clear written communication and a collaborative approach to gathering requirements, asking for feedback and resolving technical problems. Bonus Points Hands-on experience with ML models using PyTorch or similar frameworks, such as running inference or analyzing prediction errors; experience in autonomy, robotics or simulation. Experience with distributed data processing or analytical storage, such as Spark, Databricks, Delta Lake or comparable systems. Experience with AWS, Docker and GitLab CI, or with developer tools, experiment tracking, data lineage or results visualization. Prior autonomy or robotics experience is welcome but is not required for this role.