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About the Role
We are seeking an ML/RL Engineer to join our Algo team and drive the development of our unified behavioral architecture. This role bridges simulation and the real world by developing a scalable policy framework for L4 ego-policy and simulated agents, working at the intersection of Multi-Agent Reinforcement Learning (MARL) and safety-critical system design.
Responsibilities
- Develop and train diverse, conditioned policies that simulate realistic driving behaviors to stress-test and validate our autonomous driving stack.
- Lead the research and implementation of advanced RL algorithms to ensure safety metrics are treated as primary constraints in the learning process.
- Collaborate with cross-functional teams to design robust reward functions and evaluation metrics that balance safety, progress, and comfort.
- Contribute to the optimization of our large-scale, high-throughput training environments to enable rapid iteration on complex multi-agent scenarios.
- Advance our state-of-the-art neural architectures to improve spatial reasoning, long-horizon planning, and interaction modeling.
- Work closely with Simulation and Planning teams to integrate research-grade models into production-quality, safety-critical software.
Requirements
- Proven track record of training and deploying deep RL algorithms (e.g., PPO, SAC) for complex, real-world robotic or autonomous systems.
- Expertise in Python and PyTorch; strong understanding of modern deep learning architectures and optimization techniques.
- MS or PhD in Computer Science, Robotics, or a related quantitative field.
- Ability to diagnose and solve fundamental challenges in RL training, such as variance management and distribution shift.
- Experience with constrained optimization or safety-critical learning frameworks.
- Background in MARL training stability, including self-play and decentralized execution strategies.
- Familiarity with vehicle dynamics and behavior planning, particularly for long-haul highway environments.
Skills
- Python
- PyTorch
- Deep Learning
- Optimization Techniques
- Reinforcement Learning
- Multi-Agent Reinforcement Learning (MARL)
- Behavioral Modeling
- Safety-Constrained Learning
- Reward & Objective Design
- Scalable Training Pipelines
- Model Architecture
- Spatial Reasoning
- Long-Horizon Planning
- Interaction Modeling
- Constrained Optimization
- Safety-Critical Learning
- MARL Training Stability
- Self-Play
- Decentralized Execution
- Vehicle Dynamics
- Behavior Planning
Education Level
- MS or PhD in Computer Science, Robotics, or a related quantitative field.
Salary/Compensations
- Competitive salary based on experience, with opportunities for performance bonuses and equity.
Benefits
- Comprehensive health insurance
- Paid time off
About the Company
- At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe.
- With the agility of a startup and the wisdom of seasoned experts, our team has achieved numerous world-firsts and unparalleled innovations.
- United by a shared vision, we create groundbreaking solutions that propel the future of transportation.
- Join us and transform your ideas into reality.