Lead Engineer, Reinforcement Learning – Scenario Generation
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Job Description
Lead Engineer designing RL training pipelines and synthetic environments for robotics at Serve Robotics. Focus on developing algorithms for terrain navigation and social interactions in dynamic scenarios.
Responsibilities:
- 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:
- Offers Equity
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