Bot Auto

ML/RL Engineer, Behavior Planning

Houston, TX or San Francisco Bay Area

Sponsorship not specifiedDetected 19 days ago

PythonAlgorithmsMachine LearningDeep LearningPyTorchRoboticsResearchCollaboration

About the role

You will work at the intersection of Multi-Agent Reinforcement Learning (MARL) and safety-critical system design to ensure our autonomous semi-trucks navigate highways with superhuman safety and precision.

Behavioral Modeling: Develop and train diverse, conditioned policies that simulate realistic driving behaviors to stress-test and validate our autonomous driving stack.
Reward & Objective Design: Collaborate with cross-functional teams to design robust reward functions and evaluation metrics that balance safety, progress, and comfort.
Develop and train diverse, conditioned policies that simulate realistic driving behaviors to stress-test and validate our autonomous driving stack.
Collaborate with cross-functional teams to design robust reward functions and evaluation metrics that balance safety, progress, and comfort.
We are seeking a ML/RL Engineer to join our Algo team and drive the development of our unified behavioral architecture.
In this role, you will help bridge the gap between simulation and the real world by developing a scalable policy framework that represents both our L4 ego-policy and a diverse population of simulated agents.

Professional RL Experience: Proven track record of training and deploying deep RL algorithms (e.g., PPO, SAC) for complex, real-world robotic or autonomous systems.
Technical Mastery: Expertise in Python and PyTorch
Scientific Intuition: Ability to diagnose and solve fundamental challenges in RL training, such as variance management and distribution shift.
Ability to diagnose and solve fundamental challenges in RL training, such as variance management and distribution shift.
Familiarity with vehicle dynamics and behavior planning, particularly for long-haul highway environments.

Multi-Agent Systems: Background in MARL training stability, including self-play and decentralized execution strategies.
Autonomous Driving Domain: Familiarity with vehicle dynamics and behavior planning, particularly for long-haul highway environments.
Additional Information

Competitive salary based on experience, with opportunities for performance bonuses and equity.

Comprehensive health insurance, paid time off, and the opportunity to work at the forefront of the autonomous trucking industry.
United by a shared vision, we create groundbreaking solutions that propel the future of transportation.
Compensation: Competitive salary based on experience, with opportunities for performance bonuses and equity.
Safety-Constrained Learning: Lead the research and implementation of advanced RL algorithms to ensure safety metrics are treated as primary constraints in the learning process.

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.

This listing is sourced directly from Bot Auto's careers page and normalized into a canonical job model.