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 senior metrics-platform engineer, you will design, build and operate a substantial part of the platform that makes autonomy evaluation consistent and reproducible. Working directly with Perception, Planning and Systems engineers, you will connect model experiments to reliable metrics and help turn insights about model behavior, failure modes and generalization into improvements in models and data. This is a hands-on software engineering role. You will own projects through design, implementation, rollout and operation, make practical system-design trade-offs, and mentor teammates. As an early member of a newly formed team, you will help establish the engineering practices and interfaces that make the platform useful across Autonomy. Responsibilities Own the design and delivery of a metrics-platform area, such as the metric registry, computation services or comparison and reporting APIs, in partnership with the team’s technical lead. Design versioned metric definitions and data models for inputs, aggregation, slicing and results. Preserve provenance linking official evaluation runs to code, model, dataset and scenario versions. Build distributed computation and storage paths with idempotent execution, incremental processing and controlled backfills. Make informed trade-offs across throughput, latency, cost and reproducibility. Build comparison tools that expose coverage, sample sizes, uncertainty and changes in evaluation inputs. Help users distinguish model regressions from data, simulator or infrastructure failures. Partner with model developers to design evaluation experiments, inspect predictions and analyze failure patterns. Use the findings to guide model and data improvements, and build reusable evaluation harnesses that shorten the iteration cycle. Integrate metrics and regression checks into replay and closed-loop evaluation workflows and CI. Partner with domain owners on metric semantics and thresholds, and with Systems on evidence for release decisions. Deliver self-service APIs and scorecards backed by consistent definitions and results. Migrate existing consumers incrementally while preserving reporting continuity and traceability. Own reliability and performance for your services through testing, observability, incident investigation and operational documentation; measure improvements in turnaround time, correctness and cost. Write and review production code, lead design reviews for your projects, mentor engineers, and collaborate across Palo Alto, London and Belgrade to deliver software that users adopt. Qualifications 5+ years of professional software engineering experience, or equivalent demonstrated experience and impact, including ownership of production backend or data-intensive systems. Hands-on experience building software for autonomy or robotics, with direct exposure to evaluation, simulation, perception, planning or validation workflows. Strong Python programming skills, with the ability to write, test, profile and debug maintainable production software. Practical SQL skills, with AI coding tools expected to support writing and refining queries; ability to validate query logic, results and performance. Demonstrated system-design ability across service APIs, data modeling, distributed processing and storage, including failure recovery, consistency and performance trade-offs. Experience taking a project from requirements and design through implementation, rollout and operation, coordinating dependencies and communicating trade-offs clearly. Practical understanding of model inference and evaluation, including held-out datasets, generalization, reproducibility and model versioning, and the differences between open-loop replay, closed-loop simulation and real-world measurements. Ability to reason about sampling, confidence intervals, noisy metrics and valid comparisons, and to work with domain experts when stronger statistical analysis is needed. Experience with cloud infrastructure, containers, CI/CD and production observability, plus a track record of constructive code review and mentoring. Bonus Points Experience building metric registries, evaluation services, experiment-tracking systems or automated regression gates used by multiple engineering teams. Experience with Spark, Databricks, Delta Lake or comparable distributed data systems, and with AWS, Docker or GitLab CI. Experience migrating analytical or reporting systems without breaking downstream consumers, or linking metrics to scenario-level debugging and triage. Experience with PyTorch or similar ML frameworks, evaluation of end-to-end driving models, or connecting offline model metrics to closed-loop and real-world driving behavior. Pay Disclosure Salary Range for California Based Applicants: $179,000-$223,800 (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. Own the design and delivery of a metrics-platform area, such as the metric registry, computation services or comparison and reporting APIs, in partnership with the team’s technical lead. Design versioned metric definitions and data models for inputs, aggregation, slicing and results. Preserve provenance linking official evaluation runs to code, model, dataset and scenario versions. Build distributed computation and storage paths with idempotent execution, incremental processing and controlled backfills. Make informed trade-offs across throughput, latency, cost and reproducibility. Build comparison tools that expose coverage, sample sizes, uncertainty and changes in evaluation inputs. Help users distinguish model regressions from data, simulator or infrastructure failures. Partner with model developers to design evaluation experiments, inspect predictions and analyze failure patterns. Use the findings to guide model and data improvements, and build reusable evaluation harnesses that shorten the iteration cycle. Integrate metrics and regression checks into replay and closed-loop evaluation workflows and CI. Partner with domain owners on metric semantics and thresholds, and with Systems on evidence for release decisions. Deliver self-service APIs and scorecards backed by consistent definitions and results. Migrate existing consumers incrementally while preserving reporting continuity and traceability. Own reliability and performance for your services through testing, observability, incident investigation and operational documentation; measure improvements in turnaround time, correctness and cost. Write and review production code, lead design reviews for your projects, mentor engineers, and collaborate across Palo Alto, London and Belgrade to deliver software that users adopt. 5+ years of professional software engineering experience, or equivalent demonstrated experience and impact, including ownership of production backend or data-intensive systems. Hands-on experience building software for autonomy or robotics, with direct exposure to evaluation, simulation, perception, planning or validation workflows. Strong Python programming skills, with the ability to write, test, profile and debug maintainable production software. Practical SQL skills, with AI coding tools expected to support writing and refining queries; ability to validate query logic, results and performance. Demonstrated system-design ability across service APIs, data modeling, distributed processing and storage, including failure recovery, consistency and performance trade-offs. Experience taking a project from requirements and design through implementation, rollout and operation, coordinating dependencies and communicating trade-offs clearly. Practical understanding of model inference and evaluation, including held-out datasets, generalization, reproducibility and model versioning, and the differences between open-loop replay, closed-loop simulation and real-world measurements. Ability to reason about sampling, confidence intervals, noisy metrics and valid comparisons, and to work with domain experts when stronger statistical analysis is needed. Experience with cloud infrastructure, containers, CI/CD and production observability, plus a track record of constructive code review and mentoring. Bonus Points Experience building metric registries, evaluation services, experiment-tracking systems or automated regression gates used by multiple engineering teams. Experience with Spark, Databricks, Delta Lake or comparable distributed data systems, and with AWS, Docker or GitLab CI. Experience migrating analytical or reporting systems without breaking downstream consumers, or linking metrics to scenario-level debugging and triage. Experience with PyTorch or similar ML frameworks, evaluation of end-to-end driving models, or connecting offline model metrics to closed-loop and real-world driving behavior.
