Machine Learning Engineer (Reinforcement Learning)

• 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

Benefits

• Comprehensive medical, dental, and vision insurance
• Generous PTO and paid holidays
• Daily meals and bi-weekly happy hours
• Equity opportunity
• Retirement plans
• Frequent tech talks

• 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)
• Experience with large-scale distributed training and large-scale data processing
• Ability to lead ambiguous technical work from problem framing through reliable delivery
• M.S. or Ph.D. in Computer Science, Machine Learning, AI, or a related field—or equivalent practical experience
• Demonstrated impact via strong publications or a clear history of shipping impactful ML systems end-to-end
• Depth in deep learning, sequence modeling, and generative models
• 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