Lead Engineer, Reinforcement Learning & Scenario Generation
At Serve Robotics, we’re reimagining how things move in cities. Our personable sidewalk robot is our vision for the future. It’s designed to take deliveries away from congested streets, make deliveries available to more people, and benefit local businesses.
The Serve fleet has been delighting merchants, customers, and pedestrians along the way in Los Angeles, Miami, Dallas, Atlanta and Chicago while doing commercial deliveries. We’re looking for talented individuals who will grow robotic deliveries from surprising novelty to efficient ubiquity.
WHO WE ARE
We are tech industry veterans in software, hardware, and design who are pooling our skills to build the future we want to live in. We are solving real-world problems leveraging robotics, machine learning and computer vision, among other disciplines, with a mindful eye towards the end-to-end user experience. Our team is agile, diverse, and driven. We believe that the best way to solve complicated dynamic problems is collaboratively and respectfully.
The Lead Engineer, RL Scaling & Procedural Scenario Generation is responsible for building scalable training pipelines and generating high-fidelity synthetic scenarios. This role designs procedural simulation environments, creates diverse long-tail edge cases, and optimizes RL systems to train robust foundational models. This role sits at the intersection of simulation, machine learning, distributed systems, and content generation and has a high impact on how quickly and safely agents learn in simulation.
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 autom...