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Bot Auto

Algorithm Engineer, Reinforcement Learning

Houston, TX or San Francisco Bay AreamidAdded 4 weeks ago

About this role

Bot Auto seeks an ML/RL Engineer to develop behavior planning systems for autonomous semi-trucks. You'll design safety-constrained reinforcement learning policies, build scalable multi-agent training pipelines, and bridge simulation with real-world autonomous driving deployment.

What you'll do

  • Develop and train diverse, conditioned policies for realistic driving behavior simulation and stress-testing
  • Research and implement safety-constrained RL algorithms with safety as primary learning constraints
  • Design reward functions and evaluation metrics balancing safety, progress, and comfort
  • Optimize large-scale, high-throughput training environments for multi-agent scenarios
  • Advance neural architectures for spatial reasoning, long-horizon planning, and agent interaction modeling
  • Collaborate with Simulation and Planning teams to integrate research models into production systems

What they're looking for

  • Deep Reinforcement Learning (PPO, SAC, and similar algorithms)
  • Python and PyTorch
  • Multi-Agent Reinforcement Learning (MARL)
  • Safety-critical system design
  • Neural network architecture design
  • Reward engineering and objective design
  • Distributed training systems optimization
  • Autonomous driving domain knowledge (preferred)

Benefits

  • Competitive salary based on experience
  • Performance bonuses and equity opportunities
  • Comprehensive health insurance
  • Paid time off
  • Work on cutting-edge autonomous trucking technology
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Bot Auto

Bot Auto builds autonomous truck technology, developing the mechanical systems, deep learning models, and operational software that power self-driving commercial freight vehicles. The company is hiring mechanical engineers, machine learning engineers, software engineers, and interns to work across hardware design, perception and control systems, ML infrastructure, and fleet management platforms.

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Likely interview questions

  • Walk us through a deep RL project where you trained and deployed an algorithm like PPO or SAC. What were the main challenges you faced, and how did you diagnose and solve them?
  • How would you approach designing a reward function that balances safety constraints, progress, and comfort for autonomous truck behavior planning?
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