Machine Learning Engineer, Zoox

• PhD or Master’s in computer science, robotics, machine learning, or a related field
• Deep understanding of reinforcement learning and its application in simulated or robotic environments
• Hands-on experience developing, training, and fine-tuning deep learning models using modern frameworks (e.g., JAX or PyTorch)
• Strong proficiency in C++ and Python for building and deploying production machine learning systems
• Experience analyzing and bridging fidelity gaps between synthetic training data and real-world execution

Desirable

• As a Machine Learning Engineer on the Simulation Core Team, you will focus on the intersection of machine learning and synthetic environments within our high-speed, GPU-based simulation framework
• Our success depends on you driving ML efficiency while solving complex "sim-to-sim" and "sim-to-real" fidelity gaps, ensuring our safety-critical models train on data that perfectly aligns with physical vehicle behavior
• Develop and optimize our GPU-based simulation framework to support complex machine learning training and validation pipelines
• Apply reinforcement learning concepts to solve complex behavioral and path planning challenges in simulation environments
• Identify and resolve "sim-to-sim" and “sim-to-real” fidelity gaps to ensure parity between high-speed ML simulations, high-fidelity 3D environments, and physical vehicle execution
• Build systems that allow autonomy users to self-serve data generation and accelerate their training iterations
• Write robust, production-ready code to integrate advanced ML algorithms directly into our core simulation architecture