Role SummaryWe are seeking a Senior Machine Learning Engineer to drive behavioral optimization and alignment for our Large Driving Model (LDM). In this role, you will play a foundational part in the LDM Behavioral Alignment team, taking a holistic, vertical view of complex autonomous driving behaviors. Your objective is to analyze, define, and systematically improve vehicle actions by working fluidly across data, training, and model stacks.
Rather than focusing on an isolated layer, you will target the root causes of insufficient driving performance and implement end-to-end solutions-ranging from advanced dataset curation to reinforcement learning (RL)-to directly deliver safer, smoother, and more predictable on-road behavior.
Responsibilities- Diagnose performance bottlenecks for targeted driving behaviors, establishing clear solution roadmaps across data, modeling, training recipe, and reinforcement learning loops.
- Design data mining strategies and metrics to understand targeted model behavior to drive performance insights, measure progress, and inform model fine-tuning and alignment.
- Design, experiment with, and deliver RL algorithms, Supervised Fine-Tuning (SFT) strategies, and RL reward structures to optimize on-road driving policies.
- Partner across the Autonomy organization with Infrastructure, Perception, and Motion Planning teams to implement high-impact solutions while successfully handing off long-term platform productionization to system owners.
- Provide technical guidance, code reviews, and mentorship for junior and mid-level engineers, fostering high standards for system design.
Qualifications- B.S. or M.S. in Computer Science, Machine Learning, Robotics, or related fields.
- 5+ years of experience as an ML Engineer, Motion Planning Engineer, or Data Engineer within the LLM/VLM, autonomous driving, robotics, or computer vision space.
- Advanced proficiency in Python and deep learning frameworks (e.g., PyTorch), with a track record of training and fine-tuning large-scale models.
- Experience with Reinforcement Learning (RL), reward design, and policy optimization.
- Strong understanding of traditional planning concepts, edge cases, kinematic feasibility, and vehicle physics.
- Demonstrated ability to navigate highly ambiguous projects, shifting focus dynamically to solve vertical problems and deliver validated on-road impact.
- Strong cross-functional communication skills, with an ability to clearly balance technical trade-offs in a fast-paced environment.
Pay DisclosureThe salary range for this role is $179,000.00 - $223,800.00 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.
You can apply for this role through careers.rivian.com (or through internal-careers-rivian.icims.com if you are a current employee). This job is not expected to be closed any sooner than June 20, 2026.
