Principal RL Engineer, E2E Autonomy

Role overview Lead the development of end-to-end reinforcement-learning systems for autonomous machines. The role combines research and production engineering: shaping training and inference pipelines across vehicle and cloud environments, developing new models and features, and helping define the direction of a fast-moving autonomy program. This is a full-time remote position with visa sponsorship available.

Responsibilities - Design and implement online and offline reinforcement-learning methods for complex, long-horizon tasks. - Develop reward models and exploration strategies that balance performance with strict safety requirements. - Shape training and inference architecture across on-vehicle, off-vehicle, on-premises, and cloud environments. - Contribute to new end-to-end autonomy models, capabilities, and production features. - Work across conventional engineering boundaries to identify and solve ambiguous technical problems. - Collaborate closely with a lean team while rapidly evaluating ideas and learning unfamiliar areas.

Requirements - Advanced experience applying reinforcement learning to complex autonomous systems or similarly challenging environments. - Ability to design both learning algorithms and the surrounding training or inference infrastructure. - Strong judgment around reward design, exploration, performance evaluation, and safety constraints. - Comfort operating with ambiguity, defining priorities, and working across research and engineering disciplines.