Senior Algorithm Engineer, Reinforcement Learning

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 **Senior 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. ### **Why Bot Auto?** We are a small, hyper-focused team on a mission to beat human cost-per-mile through technology. We recently successfully completed the industry’s first fully humanless commercial truckload, proving that our vision is a reality. If you are passionate about AI, safety, and transforming logistics, we want to hear from you.