Senior/Staff Deep Reinforcement Learning Engineer

About the role

Our DD Labs team builds real-time autonomous delivery systems. The Planning & Decision-Making group is investing heavily in deep reinforcement learning to move beyond classical planning, learning policies that generalize across novel driving scenarios, handle long-tail edge cases, and improve continuously from large-scale fleet data. Our models jointly handle prediction and planning in a single unified architecture. Our stack is pure JAX end-to-end: the same code you train with is the code that runs on the robot. No C++ rewrites, no TensorRT export. A new policy goes from training to on-vehicle deployment in minutes. As a Senior/Staff Deep RL Engineer, you will design, train, and deploy deep reinforcement learning policies that make real-time driving decisions for our autonomous vehicles. You will own the full lifecycle, from problem formulation and reward design through large-scale distributed training to on-vehicle inference. You'll help define how learned components compose with the rest of the autonomy stack to produce robust, shippable behavior. You're excited about this opportunity because you will... Formulate complex driving tasks as RL problems with well-shaped reward functions and expressive state/action representations. Design and train model-based deep RL agents using GPU-accelerated simulation at massive scale, including improving the simulator itself. Build and maintain distributed training infrastructure in JAX across large compute clusters. Build agentic optimization systems that automatically improve code, run experiments, analyze metrics, and iterate on RL policies with minimal human intervention. We're excited about you because... BS/MS/PhD in CS, EE, Robotics, or a related field, with a strong foundation in reinforcement learning and deep learning. You have proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software Hands-on experience training RL agents at scale, ideally in robotics, autonomous driving, or other real-time decision-making domains. Proficiency in JAX or a similar functional ML framework; comfort with JIT compilation, vectorized environments, and GPU-accelerated simulation. Deep grasp of core RL concepts: policy gradients, value functions, exploration-exploitation, model-based RL, reward shaping, and sim-to-real transfer. Data-driven mindset: comfortable building experiment pipelines, analyzing training runs, and letting metrics guide architectural decisions.

Requirements

• Publications at top venues (NeurIPS, ICML, ICLR, CoRL, RSS, ICRA) on RL or learned planning.
• Experience building or working with GPU-accelerated simulators for RL training.
• Track record of shipping a learned component in a production robotics or autonomous vehicle stack.
• Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only
• The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: Covey

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Company Intel

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