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.

- 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.

- 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.