Machine Learning Engineering TL, Behavior Planning

Aurora Innovation•Pittsburgh, PA

About The Position

Aurora hires talented people with diverse backgrounds who are ready to help build a transportation ecosystem that will make our roads safer, get crucial goods where they need to go, and make mobility more efficient and accessible for all. We are seeking a ML Engineering TL to join the Behavior Planning Team . This is a high-impact role where you will sit at the frontier of autonomous vehicle technology. You won't just be applying AI and ML methods; you will be advancing the state-of-the-art for how a self-driving system reasons about the world, interacts with humans on the road, and masters complex, high-dimensional decision-making. In this role, you will Define the architecture of our onboard planning models: Develop and deploy large-scale models trained with Imitation Learning and Reinforcement Learning that enable the Aurora Driver to navigate complex environments with human-like fluidity and superhuman safety. Build our next-generation simulation engine: Architect cutting-edge offboard foundation models that power our simulation engine, creating realistic "world models" to test the Aurora Driver against an infinite variety of edge cases. Revolutionize evaluation: Develop powerful offboard critic models that can evaluate driving behavior at scale, identifying subtle nuances in comfort, progress, and safety that traditional heuristics miss. Bridge research and production: Reach new frontiers of autonomous driving technology by pushing forward the state-of-the-art, but also deploy your models on real production vehicles that drive on public roads and must meet the highest standards of safety. Mentor and lead: Serve as a technical lead, guiding junior engineers and shaping the long-term roadmap for ML-based planning at Aurora, including the onboard and off-board ecosystem that is needed to support it.

Requirements

MS or PhD in Robotics, Machine Learning, Computer Science, or a related quantitative field, or equivalent practical experience.
8 + years of experience developing state-of-the-art ML models, either in a research or production setting.
Hands-on experience working on Imitation Learning or Reinforcement Learning applied to physical or simulated agents.
Experience training large models on massive datasets using distributed computing.
Fluency in Python, with a focus on writing high-performance, maintainable code.
Deep experience with PyTorch (preferred) or another modern ML framework, and a mastery of modern ML architectures including Transformers and Diffusion Models.

Nice To Haves

A track record of publications in top-tier ML conferences (NeurIPS, ICML, CoRL, CVPR, AAAI).
Experience deploying complex ML systems in production environments.
Experience in developing generative models or neural simulators for synthetic data generation.
Experience leading small or large teams to execute highly technical projects.