Lead Engineer, Reinforcement Learning – Scenario Generation

Job not on LinkedIn

🕒 December 19, 2025

🏄 California – Remote

🏄 California – Remote

💵 $190k - $230k / year

⏰ Full Time

🟠 Senior

🧑‍💻 Full-stack Engineer

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Serve Robotics

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2017

🚗 Transport

🤖 Artificial Intelligence

💰 $30M Venture Round on 2023-08

Transport • Artificial Intelligence

Serve Robotics is an innovative company focused on revolutionizing the delivery industry with its autonomous delivery robots. The company aims to make delivery services more affordable, sustainable, and convenient by using self-driving robots instead of traditional two-ton vehicles for small deliveries like burritos. Through a commercial deal with Uber, Serve Robotics plans to deploy up to 2,000 robots, marking a significant advancement in the autonomous delivery sector.

📋 Description

• 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