Senior Staff Research Engineer – Reinforcement Learning for AI Agents

Key Responsibilities

• Reinforcement learning methods for LLM-driven agents and decision systems.
• Policy optimization for long-horizon reasoning and planning.
• Learning from human or AI feedback (RLHF / RLAIF).
• Agent training pipelines built on top of our agent infrastructure platform.
• Evaluation and benchmarking systems for agent capabilities.
• Learning loops that integrate real-world and simulation data.
• Contribute to AI systems that continuously improve after deployment.

Basic Qualifications

• MS or PhD in Computer Science, AI, Machine Learning, Robotics, or a related field.
• Strong background in reinforcement learning or machine learning.
• Experience implementing RL algorithms such as PPO, Actor-Critic, or policy gradient methods.
• Strong programming skills in Python with PyTorch or JAX.
• Experience building ML training systems or infrastructure.

Preferred Qualifications

• Experience with RLHF or preference learning.
• Experience with LLM agents or tool-using AI systems.
• Multi-agent systems or long-horizon planning.
• Simulation environments for RL.
• Publishations in NeurIPS, ICML, ICLR, ACL, or related venues.

What Do We Provide

• A fun, supportive and engaging environment.
• Opportunity to make significant impact on transportation revolution by the means of advancing autonomous driving.
• Opportunity to work on cutting edge technologies with the top talent in the field.
• Competitive compensation package.
• Snacks, lunches and fun activities.

Pay

The base salary range for this full-time position is $244,140 - $413,160, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.