Machine Learning Engineer - Reinforcement Learning

What you'll do

• Develop scalable training and fine-tuning systems and RL methods for realistic driving behaviors and simulation-aligned evaluation.
• Build and operate production ML models and data/evaluation loops for behavior triage, anomaly detection, and fleet-scale performance assessment.
• Partner with Prediction, Planning, Research, and platform teams to deliver cross-cutting improvements with measurable outcomes.

What they ask for

• M.S. or Ph.D. in Computer Science, Machine Learning, AI, or a related field—or equivalent practical experience.
• Production-grade ML experience with a strong RL component, such as policy learning, preference/feedback optimization, or offline/online RL pipelines.
• Depth in deep learning, sequence modeling, and generative models; experience with large-scale distributed training and data processing.
• Demonstrated impact through strong publications or end-to-end delivery of impactful ML systems.

Nice to have

• Background in autonomous vehicles, robotics, or complex simulation environments.
• Experience with modern RL and post-training techniques, simulation-platform integration, or metrics for safety-critical AI systems.

Pay

$150,000 - $250,000 USD base salary

Tools

LLM, VLM, RLHF, dLLM, VLA, Python, PyTorch

AI focus

Applies reinforcement learning, generative models, and LLM/VLM methods to generate and evaluate driving behaviors, automate behavior analysis, and improve autonomous-driving performance assessment.