Algorithm Engineer, Reinforcement Learning

Bot Auto; Houston, San Francisco (TX, CA); On-site; USD 100,000 - 150,000; Full time

Benefits offered by this job

Comprehensive health insurance

Paid time off

Performance bonuses

Equity opportunities

Job summary

Bot Auto is seeking an ML/RL Engineer for our Algo team in Houston, Texas. In this innovative role, you will drive development on a unified behavioral architecture incorporating cutting-edge research in Multi-Agent Reinforcement Learning and safety-critical systems, ensuring our autonomous trucks operate with unmatched safety.

The ideal candidate will have experience in deploying deep RL algorithms like PPO and SAC, and expertise in Python and PyTorch. Join us at Bot Auto to work at the forefront of autonomous transportation.

Qualifications

• Proven track record of training and deploying deep RL algorithms for complex robotic or autonomous systems.
• Expertise in Python and PyTorch with a solid grasp of deep learning architectures and optimization techniques.
• Ability to tackle challenges in RL training, such as variance management and distribution shift.

Responsibilities

• Develop and train conditioned policies to simulate realistic driving behaviors.
• Lead research on RL algorithms ensuring safety metrics are primary constraints.
• Collaborate on designing reward functions and metrics for safety and comfort.
• Optimize large-scale training environments for rapid iteration.
• Improve neural architectures for spatial reasoning and planning.
• Integrate models into safety-critical software with other teams.

Skills

Deep RL algorithms (PPO, SAC)

Python

PyTorch

Problem-solving in RL

Education

MS or PhD in Computer Science, Robotics, or related

Job description

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.

Role Overview

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. 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.

Key Responsibilities

• Behavioral Modeling: Develop and train diverse, conditioned policies that simulate realistic driving behaviors to stress‑test and validate our autonomous driving stack.
• 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.
• Reward & Objective Design: Collaborate with cross‑functional teams to design robust reward functions and evaluation metrics that balance safety, progress, and comfort.
• Scalable Training Pipelines: Contribute to the optimization of our large‑scale, high‑throughput training environments to enable rapid iteration on complex multi‑agent scenarios.
• Model Architecture: Advance our state‑of‑the‑art neural architectures to improve spatial reasoning, long‑horizon planning, and interaction modeling.
• Cross‑Team Collaboration: Work closely with Simulation and Planning teams to integrate research‑grade models into production‑quality, safety‑critical software.

Required Qualifications

• 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; strong understanding of modern deep learning architectures and optimization techniques.
• Academic Background: MS or PhD in Computer Science, Robotics, or a related quantitative field.
• Scientific Intuition: Ability to diagnose and solve fundamental challenges in RL training, such as variance management and distribution shift.

Preferred Qualifications

• Safe RL Specialization: Experience with constrained optimization or safety‑critical learning frameworks.
• 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

• Compensation: Competitive salary based on experience, with opportunities for performance bonuses and equity.
• Benefits: Comprehensive health insurance, paid time off, and the opportunity to work at the forefront of the autonomous trucking industry.

As set forth in Bot Auto’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.