Serve Robotics

Lead Engineer, Reinforcement Learning – Scenario Generation

Serve Robotics

full-time

Posted on:

Location Type: Remote

Location: Remote • California • 🇺🇸 United States

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Salary

💰 $190,000 - $230,000 per year

Job Level

Senior

Tech Stack

CloudKubernetesPythonPyTorchRayUnity

About the role

  • Develop RL algorithms that can help with terrain intelligence and social navigation behaviors.
  • Design, build, and optimize large-scale RL training pipelines (distributed compute, GPU clusters, containerized workflows).
  • Implement curriculum learning, domain randomization, and multi-agent RL strategies.
  • Optimize RL model performance, sample efficiency, and stability across thousands to millions of simulation steps.
  • Build automated tools for experiment orchestration, rollout collection, and metrics visualization.
  • Develop procedural generation pipelines for synthetic environments, agents, and dynamic behaviors.
  • Build tools to generate long-tail scenarios, sudden appearance of objects, traffic behaviors, rare events, and environmental variations.
  • Create systems for configuration, validation, and scoring of generated scenarios.
  • Collaborate with autonomy, ML, and safety teams to map real-world failures into repeatable synthetic simulation cases.
  • Design APIs to connect RL agents, scenario generators, planners, and environment simulators.
  • Debug and optimize simulation performance (real-time speed, determinism, reproducibility).
  • Work with 3D assets, traffic models, mapping systems (e.g., Isaac Sim, CARLA, Unity, Gazebo).
  • Partner with autonomy, data, and modeling teams to define training objectives and scenario requirements.
  • Translate real-world logs and edge cases into parameterized procedural content.
  • Document tools, frameworks, and workflows for internal users.

Requirements

  • Master’s degree in Robotics, AI, Computer Science, Mathematics, or a related field.
  • 7+ years of professional experience with shipping transformer based AI models handling complex navigation or manipulation tasks in AV or robotics solutions at scale in the real world.
  • 3+ years technical leadership/architecture experience
  • Strong experience with Reinforcement Learning (PPO, SAC, A3C, DQN, multi-agent RL, or equivalents).
  • Hands-on experience with distributed training frameworks (Ray RLlib, Accelerate, PyTorch Distributed, Kubernetes, or similar).
  • Proficiency in Python and C++ for performance-critical simulation or graphics pipelines.
  • Experience building or modifying simulation environments (Isaac Sim, Unity, Unreal, CARLA, Gazebo, MuJoCo or custom engines).
  • Experience with procedural generation (noise functions, rule-based systems, agent scripts, behavior trees).
  • Experience with GPU compute, containers, and cloud infrastructure.
Benefits
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Applicant Tracking System Keywords

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Hard skills
Reinforcement LearningPPOSACA3CDQNdistributed training frameworksPythonC++procedural generationGPU compute
Soft skills
technical leadershipcollaborationdocumentation