machine learning engineer for autonomous driving

Rivian develops emissions-free Electric Adventure Vehicles and advanced machine learning algorithms for safety-critical self-driving features.

• Guide the architecture, implementation, and deployment of foundation models that act as learned world models;
• Develop technical strategy and architecture for foundation models as unified world models;
• Develop multi-modal, multi-task transformer-based systems that support closed-loop autonomy;
• Build training and evaluation pipelines at scale across petabytes of real-world and simulated driving data;
• Collaborate with cross-functional teams across perception, planning, simulation, and ML infrastructure;
• Drive alignment between model capabilities and real-world deployment constraints, including latency, robustness, and validation;
• Publish internal technical guidance and mentor engineers across autonomy ML.

• B.S., M.S., or Ph.D. in Computer Science, Robotics, or a related field;
• 7+ 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, including imitation learning, behavior cloning, or world models; experience with sensor fusion using LiDAR, camera, or radar in a learned model.

• No conditions specified