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 We are seeking a Staff Machine Learning Engineer interested in the development of end-to-end models that unify perception, prediction, and planning in a single system. This role is ideal for someone excited by the challenge of scaling models that learn from vast sensory data to enable autonomous driving. Ideal candidates have experience with Supervised Learning, Reinforcement Learning, and/or LLMs. Responsibilities As a member of the Autonomy team, you will guide the architecture, implementation, and deployment of the Large Driving Model (LDM). This model will support not only decision-making and closed-loop autonomy . Key areas of responsibility include: Developing technical strategy and architecture for end-to-end autonomous driving model Developing multi-modal, multi-task transformer-based systems that support closed-loop autonomy Building training and evaluation pipelines at scale across petabytes of real-world and simulated driving data Collaborating with cross-functional teams across perception, planning, simulation, and ML infrastructure Driving alignment between model capabilities and real-world deployment constraints (latency, robustness, validation) Publishing internal technical guidance and mentoring engineers across autonomy ML Qualifications B.S., M.S., or Ph.D. in Computer Science, Robotics, or a related field 5+ years of experience building and deploying large-scale ML systems Deep understanding of foundation models, self-supervised learning, and world models in robotics or simulation Strong software engineering background, with fluency in Python and C++ Experience training and evaluating transformer models or end-to-end autonomous agents Familiarity with real-time inference systems and autonomous vehicle constraints Proven leadership in driving ML projects from research to production Bonus Qualification Prior work on end-to-end autonomous driving architectures (e.g., imitation learning, behavior cloning, world models) Experience with sensor fusion (LiDAR, camera, radar) in a learned mode Pay Disclosure Salary Range for California Based Applicants: $228,000 - $285,000 (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 premium 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. 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Staff Machine Learning Engineer, End-to-end Autonomy
Seeking a Staff Machine Learning Engineer to develop end-to-end models for autonomous driving, unifying perception, prediction, and planning. The role involves guiding the architecture, implementation, and deployment of the Large Driving Model (LDM), building large-scale training and evaluation pipelines, and collaborating with cross-functional teams to ensure real-world deployment constraints are met. Experience with foundation models, self-supervised learning, and transformer models is required.
What you'd actually do
- Developing technical strategy and architecture for end-to-end autonomous driving model
- Developing multi-modal, multi-task transformer-based systems that support closed-loop autonomy
- Building training and evaluation pipelines at scale across petabytes of real-world and simulated driving data
- Collaborating with cross-functional teams across perception, planning, simulation, and ML infrastructure
- Driving alignment between model capabilities and real-world deployment constraints (latency, robustness, validation)
Skills
Required
- B.S., M.S., or Ph.D. in Computer Science, Robotics, or a related field
- 5+ years of experience building and deploying large-scale ML systems
- Deep understanding of foundation models, self-supervised learning, and world models in robotics or simulation
- Strong software engineering background, with fluency in Python and C++
- Experience training and evaluating transformer models or end-to-end autonomous agents
- Familiarity with real-time inference systems and autonomous vehicle constraints
- Proven leadership in driving ML projects from research to production
Nice to have
- Prior work on end-to-end autonomous driving architectures (e.g., imitation learning, behavior cloning, world models)
- Experience with sensor fusion (LiDAR, camera, radar) in a learned mode
What the JD emphasized
- end-to-end autonomous driving
- Large Driving Model (LDM)
- transformer-based systems
- petabytes of real-world and simulated driving data
- real-time inference systems
- autonomous vehicle constraints
Other signals
- end-to-end autonomous driving model
- Large Driving Model (LDM)
- transformer-based systems
- petabytes of real-world and simulated driving data
- real-time inference systems
- autonomous vehicle constraints