Machine Learning Engineering TL, Behavior Planning
- 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.
- 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.
- 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.
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