Machine Learning Engineer, Reinforcement Learning

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 machine learning in production-grade settings, with a strong reinforcement learning component, such as policy learning, preference or feedback optimization, or offline/online RL pipelines
• Deep expertise in deep learning, sequence modeling, and generative models
• Demonstrated impact through strong publications or a track record of delivering impactful machine learning systems end to end
• Experience with large-scale distributed training and data processing
• Ability to lead ambiguous technical projects from problem definition through reliable delivery
• Background in autonomous vehicles, robotics, or complex simulation environments
• Strong understanding of modern reinforcement learning and post-training techniques for LLMs, dLLMs, VLAs, and video generation
• Hands-on experience integrating simulation platforms with machine learning training and evaluation workflows
• Proficiency in Python and frameworks such as PyTorch
• Experience defining and managing metrics for complex, safety-critical AI systems
• Technical leadership skills, including influencing stakeholders, aligning teams, and promoting rigorous evaluation
• Strong communication skills, including the ability to explain complex trade-offs clearly

Responsibilities:

• Build scalable systems to train and fine-tune large generative models that create realistic, informative driving behaviors for evaluation and scenario coverage
• Develop and refine reinforcement learning methods, including algorithms, reward and preference objectives, and training approaches suited to high-fidelity behaviors in simulation-aligned workflows
• Deliver deep learning solutions, including LLMs and VLMs where appropriate, to improve human-led triage, automate high-volume workflows, and analyze self-driving behavior to identify critical anomalies
• Lead production-oriented machine learning for fleet-scale assessment, including model training, optimization, monitoring, and iteration across extensive real-world exposure
• Design and improve data and evaluation systems inspired by RLHF and related approaches, turning preference and judgment signals into repeatable, scalable training and evaluation loops
• Collaborate with Prediction, Planning, Research, and platform and engineering teams to deliver cross-functional improvements measured by clear metrics

Technologies:

• AI
• Machine Learning
• Model Training
• PyTorch
• Python
• Robotics
• Fine-tuning
• Support
• LLM

More

Founded in Silicon Valley in 2016, we are a global autonomous mobility company developing and commercializing autonomous driving technologies and services. Our business units span Robotaxi, Robotruck, and Personally Owned Vehicles. We are focused on building safe autonomous driving capabilities at a growing number of sites worldwide. Our work has received industry recognition, and by August 2023 our autonomous vehicles had traveled nearly 21 million miles globally. We went public on NASDAQ in November 2024. The annual base salary range for this role is $150,000–$250,000; pay may vary based on qualifications, skills, experience, and location. The role may also be eligible for bonuses or incentives and restricted stock units. Eligible employees receive medical, dental, and vision coverage; traditional and Roth 401(k) plans; life insurance; paid vacation and public holidays; family leave; short- and long-term disability coverage; and free food and snacks.

last updated 44 week of 2026