Algorithm Engineer, Reinforcement Learning
Houston, TX or San Francisco Bay Area
USD 180K-220K (estimate) Mid-level Full Time
Tasks
- Design reward functions and evaluation metrics for safety progress and comfort
- Develop and train conditioned policies for driving behavior simulation
- Implement safety constrained reinforcement learning algorithms
- Improve neural network architectures for spatial reasoning long horizon planning and interaction modeling
- Integrate research models into production quality safety critical software
- Optimize scalable training pipelines for multi agent scenarios
Perks/Benefits
Skills/Tech-stack
Behavior planning | Constrained Learning | Constrained optimization | Decentralized Execution | Deep reinforcement learning | Distribution Shift | Function design | Multi-Agent | Multi-Agent Reinforcement Learning | Neural Networks | PPO | PyTorch | Python | Reinforcement Learning | Reward Function | Reward function design | SAC | Safety Constrained Learning | Self-play | Variance reduction | Vehicle dynamics
Education
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