Senior/Staff Deep Reinforcement Learning Engineer (JAX)

Location: San Francisco, CA, United States

Salary: $168,000–$247,000 USD per year, plus potential equity grants.

Company

hirify.global's DD Labs team builds real-time autonomous delivery systems and learned planning and decision-making components for autonomous vehicles.

What you will do

• Formulate complex driving tasks as reinforcement learning problems with reward functions and state/action representations.
• Design and train model-based deep reinforcement learning agents using GPU-accelerated simulation at large scale.
• Improve the simulation environment and build distributed training infrastructure in JAX across large compute clusters.
• Design, train, and deploy policies for real-time driving decisions, from problem formulation through on-vehicle inference.
• Build agentic optimization systems that generate code, run experiments, analyze metrics, and iterate on reinforcement learning policies.
• Define how learned components integrate with the broader autonomy stack to produce robust, production-ready behavior.

Requirements

• BS, MS, or PhD in computer science, electrical engineering, robotics, or a related field.
• Strong foundation in reinforcement learning and deep learning, including policy gradients, value functions, exploration-exploitation, model-based RL, reward shaping, and sim-to-real transfer.
• Experience training reinforcement learning agents at scale, preferably in robotics, autonomous driving, or real-time decision-making systems.
• Proficiency in JAX or a similar functional machine learning framework, including JIT compilation, vectorized environments, and GPU-accelerated simulation.
• Experience building experiment pipelines, analyzing training runs, and using metrics to guide architecture decisions.
• Proficiency with AI coding tools such as Claude Code, Codex, or Cursor across software design, code generation, testing, monitoring, and release.

Nice to have

• Publications on reinforcement learning or learned planning at venues such as NeurIPS, ICML, ICLR, CoRL, RSS, or ICRA.
• Experience building or using GPU-accelerated simulators for reinforcement learning.
• Experience shipping learned components in production robotics or autonomous vehicle stacks.

Culture & Benefits

• Comprehensive benefits for regular employees, including medical, dental, and vision coverage.
• 401(k) plan with employer matching, paid parental leave, paid time off, and paid sick leave.
• Wellness, commuter, disability, life insurance, family-forming, and mental health benefits.
• Salaried roles include flexible paid time off and 80 hours of paid sick time per year.