Machine Learning Engineer - Simulation Framework

Responsibilities

• 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.

Requirements

• 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.

Nice-to-Haves

• Experience with GPU programming (CUDA) or high-performance compute clusters.
• Automotive or autonomous robotics industry experience.
• Strong background in deterministic systems and latency optimization.