Algorithm Engineer, Reinforcement Learning

Botauto; Houston (TX); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Equity

Healthcare benefits

Performance bonuses

Job summary

Bot Auto is seeking an ML/RL Engineer to join our Algo team and drive the development of a unified behavioral architecture for autonomous trucks. You will bridge simulation and real-world deployment by building a scalable policy framework that models both our L4 ego-policy and a diverse population of simulated agents.

Collaborate on Multi-Agent RL, safety-constrained learning, and scalable training pipelines to advance state-of-the-art neural architectures for long-horizon planning and safe

Qualifications

• MS or PhD in Computer Science, Robotics, or related field.
• Experience training and deploying RL algorithms (PPO, SAC) for real-world autonomous systems.
• Strong Python and PyTorch programming skills.

Responsibilities

• Behavioral Modeling: Develop and train diverse policies to simulate driving behaviors.
• Safety-Constrained Learning: Implement RL algorithms with safety metrics as primary constraints.
• Reward & Objective Design: Design robust reward functions and evaluation metrics.
• Scalable Training Pipelines: Optimize large-scale training environments for rapid iteration.
• Model Architecture: Advance neural architectures for spatial reasoning and long-horizon planning.
• Cross-Team Collaboration: Integrate research models into production-safe software.

Skills

Reinforcement Learning

Python

PyTorch

Deep Learning

Education

MS or PhD in Computer Science/Robotics

Job description

Company Introduction

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.