Lead Engineer, Reinforcement Learning – Scenario Generation

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🕒 vor 5 Monaten

🏄 California – Remote

🏄 California – Remote

💵 $190.000 - $230.000 / Jahr

⏰ Vollzeit

🟠 Senior

🧑‍💻 Full-Stack-Entwickler

🦅 H1B-Visum-Sponsor

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Serve Robotics

WebsiteLinkedInAlle Stellen

51 - 200 Mitarbeiter

Gegründet 2017

🚗 Transport

🤖 Künstliche Intelligenz

💰 €30.000.000 Venture Round im 2023-08

Transport • Artificial Intelligence

Serve Robotics ist ein innovatives Unternehmen, das sich darauf konzentriert, die Lieferbranche mit autonomen Lieferrobotern zu revolutionieren. Das Unternehmen zielt darauf ab, Lieferdienste erschwinglicher, nachhaltiger und bequemer zu gestalten, indem es selbstfahrende Roboter anstelle traditioneller Zwei-Tonnen-Fahrzeuge für kleine Lieferungen wie Burritos einsetzt. Durch eine kommerzielle Vereinbarung mit Uber plant Serve Robotics, bis zu 2. 000 Roboter einzusetzen, was einen bedeutenden Fortschritt im Bereich der autonomen Lieferung darstellt.

Beschreibung

• 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.

🎯 Anforderungen

• 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.

🏖️ Vorteile

• Offers Equity