Lead Engineer, Reinforcement Learning – Scenario Generation

Vaga não está no LinkedIn

🕒 Dezembro 19, 2025

🏄 California – Remoto

🏄 California – Remote

💵 $190.000 - $230.000 / ano

⏰ Tempo Integral

🟠 Sênior

🧑‍💻 Engenheiro Full-stack

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Serve Robotics

SiteLinkedInTodas as Vagas

51 - 200 funcionários

Fundada em 2017

🚗 Transporte

🤖 Inteligência Artificial

💰 $30.000.000 Venture Round em 2023-08

Transport • Artificial Intelligence

Serve Robotics é uma empresa inovadora focada em revolucionar a indústria de entregas com seus robôs de entrega autônomos. A empresa visa tornar os serviços de entrega mais acessíveis, sustentáveis e convenientes ao usar robôs autônomos em vez de veículos tradicionais de duas toneladas para entregas pequenas, como burritos. Através de um acordo comercial com a Uber, a Serve Robotics planeja implantar até 2. 000 robôs, marcando um avanço significativo no setor de entregas autônomas.

Descrição

• 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.

🎯 Requisitos

• 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.

🏖️ Benefícios

• Offers Equity