# Algorithm Engineer, Reinforcement Learning at Bot Auto

Algorithm Engineer, Reinforcement Learning at Bot Auto in Houston, TX or San Francisco Bay Area. Posted June 23, 2026.

- Company: Bot Auto
- Location: Houston, TX or San Francisco Bay Area
- Posted: 2026-06-23
- Apply: https://job-boards.greenhouse.io/botauto/jobs/5277558008
- Source: https://jobmagic.ai/jobs/algorithm-engineer-reinforcement-learning-bot-auto-y2sa4y

## Requirements

- 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.

## 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.