At the core of everything we do is our commitment to safety.
Key Responsibilities
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.
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.
The base salary range for this position is $171K - $247K per year. Aurora’s pay ranges are determined by role, level, and location. Within the range, the successful candidate’s starting base pay will be determined based on factors including job-related skills, experience, qualifications, relevant education or training, and market conditions. These ranges may be modified in the future. The successful candidate will also be eligible for an annual bonus, equity compensation, and benefits.
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Working at Aurora At Aurora, we bring together extraordinarily talented and experienced people united by the strength of our values. We operate with integrity, set outrageous goals, and build a culture where we win together — all without any jerks.
We believe in-person work increases collaboration, empathy and our ability to lead effectively. As a result, we operate in a hybrid work environment where Aurorans are in office at least 3 days per week.
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