Machine Learning Engineer - Reinforcement Learning

Responsibility

• Build scalable systems for training and fine-tuning large generative models that produce realistic, informative driving behaviors for evaluation and scenario coverage.
• Implement and iterate on RL-style methods: algorithms, reward / preference objectives, and training setups suited to high-fidelity, insightful behaviors in simulation-aligned workflows (closed-loop evaluation mindset).
• Ship deep learning solutions (including LLM / VLM where appropriate) that improve human-led triaging, automate high-volume workflows, and support nuanced analysis of self-driving behavior to surface critical anomalies.
• Own production-oriented ML for fleet-scale assessment: training, optimization, monitoring, and iteration of models used to judge performance across large real-world exposure.
• Design and evolve data + evaluation systems inspired by RL from human preferences (RLHF) and related paradigms—turning preference/judgment signals into repeatable, scalable training and evaluation loops.
• Partner broadly with teams such as Prediction, Planning, Research, and platform/engineering leads to land cross-cutting improvements with clear metrics.

Requirements

• M.S. or Ph.D. in Computer Science, Machine Learning, AI, or a related field—or equivalent practical experience.
• Hands-on experience building and applying ML in production-grade settings, with a strong RL component (policy learning, preference/feedback optimization, or offline/online RL pipelines).
• Depth in deep learning, sequence modeling, and generative models.
• Demonstrated impact via strong publications or a clear history of shipping impactful ML systems end-to-end.
• Experience with large-scale distributed training and large-scale data processing.
• Ability to lead ambiguous technical work from problem framing through reliable delivery.

Preferred

• Background in autonomous vehicles, robotics, or complex simulation environments.
• Strong grasp of modern RL and post-training techniques in LLM, dLLM, VLA and video generations.
• Hands-on integration of simulation platforms with ML training and evaluation workflows.
• Python fluency and frameworks such as PyTorch
• Experience defining and operating metrics for complex, safety-critical AI systems.
• Technical leadership: influencing stakeholders, aligning teams, and raising the bar for evaluation rigor.
• Excellent communication—simple explanations of complex trade-offs.

Compensation and Benefits

Base Salary Range: $150,000 - $250,000 Annually

Compensation may vary outside of this range depending on many factors, including the candidate’s qualifications, skills, competencies, experience, and location. Base pay is one part of the Total Compensation and this role may be eligible for bonuses/incentives and restricted stock units.