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 Senior Manager of Machine Learning to build and lead a team focused on occupancy modeling as a core output of our end-to-end autonomy model. This team will develop learned representations of the driving scene that capture static and dynamic occupancy, freespace, uncertainty, and future scene evolution to support safe and scalable autonomous driving. Responsibilities As a leader on the Autonomy team, you will own the strategy, roadmap, execution, and team development for occupancy outputs within the Large Driving Model, our end-to-end autonomy architecture. The team’s work will directly influence downstream planning, simulation, validation, and closed-loop vehicle behavior. Key areas of responsibility include: Build and lead a small, high-performing ML team focused on occupancy outputs for our end-to-end autonomy model. Define the roadmap for occupancy modeling, including freespace, static and dynamic occupancy, occlusion reasoning, uncertainty, and future scene evolution. Partner closely with perception, planning, simulation, data, and ML infrastructure teams to make occupancy outputs useful for real-world autonomy. Guide model development, training, evaluation, debugging, and deployment of large-scale autonomy ML systems. Establish clear metrics and workflows that connect occupancy quality to downstream planning, safety, and closed-loop performance. Mentor engineers, set strong technical standards, and communicate progress and tradeoffs clearly across the organization. Qualifications B.S., M.S., or Ph.D. in Computer Science, Robotics, Machine Learning, or a related field. 8+ years of experience building ML systems, ideally in autonomy, robotics, perception, prediction, planning, or simulation. Experience managing or technically leading ML engineers or researchers. Strong understanding of modern autonomy ML systems, including transformer models, multi-task learning, sensor fusion, or end-to-end driving models. Experience with learned scene representations such as occupancy, freespace, BEV perception, semantic maps, trajectories, or world models. Strong ability to turn ambiguous technical problems into clear team roadmaps and execution plans. Strong engineering background, with fluency in Python and experience working with production ML systems. Pay Disclosure The salary range for this role is $265,000 - $331,000 for California based applicants. This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee’s position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, geographic location, shift, and organizational needs. The successful candidate may be eligible for annual performance bonus and equity awards. We offer a comprehensive package of benefits for full-time and part-time employees, their spouse or domestic partner, and children up to age 26, including but not limited to paid vacation, paid sick leave, and a competitive portfolio of insurance benefits including life, medical, dental, vision, short-term disability insurance, and long-term disability insurance to eligible employees. You may also have the opportunity to participate in Rivian’s 401(k) Plan and Employee Stock Purchase Program if you meet certain eligibility requirements. Full-time employee coverage is effective on their first day of employment. Part-time employee coverage is effective the first of the month following 90 days of employment. More information about benefits is available at rivianbenefits.com. Equal Opportunity Rivian is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. 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Senior Manager, ML Occupancy Modeling, Autonomy
Senior Manager to build and lead a team focused on occupancy modeling as a core output of an end-to-end autonomy model. The team will develop learned representations of the driving scene to support safe and scalable autonomous driving. Responsibilities include defining the roadmap, guiding model development, training, evaluation, debugging, and deployment of large-scale autonomy ML systems.
What you'd actually do
- Build and lead a small, high-performing ML team focused on occupancy outputs for our end-to-end autonomy model.
- Define the roadmap for occupancy modeling, including freespace, static and dynamic occupancy, occlusion reasoning, uncertainty, and future scene evolution.
- Partner closely with perception, planning, simulation, data, and ML infrastructure teams to make occupancy outputs useful for real-world autonomy.
- Guide model development, training, evaluation, debugging, and deployment of large-scale autonomy ML systems.
- Establish clear metrics and workflows that connect occupancy quality to downstream planning, safety, and closed-loop performance.
Skills
Required
- B.S., M.S., or Ph.D. in Computer Science, Robotics, Machine Learning, or a related field.
- 8+ years of experience building ML systems, ideally in autonomy, robotics, perception, prediction, planning, or simulation.
- Experience managing or technically leading ML engineers or researchers.
- Strong understanding of modern autonomy ML systems, including transformer models, multi-task learning, sensor fusion, or end-to-end driving models.
- Experience with learned scene representations such as occupancy, freespace, BEV perception, semantic maps, trajectories, or world models.
- Strong ability to turn ambiguous technical problems into clear team roadmaps and execution plans.
- Strong engineering background, with fluency in Python and experience working with production ML systems.
What the JD emphasized
- occupancy modeling
- end-to-end autonomy model
- learned scene representations
- transformer models
- multi-task learning
- sensor fusion
- end-to-end driving models
- occupancy, freespace, BEV perception, semantic maps, trajectories, or world models
Other signals
- end-to-end autonomy model
- occupancy modeling
- learned scene representations
- transformer models