Machine Learning Engineering TL, Behavior Planning
About the role
The ML Engineering TL will define the architecture of onboard planning models, develop and deploy large-scale models trained with Imitation Learning and Reinforcement Learning, architect cutting-edge offboard foundation models, and develop powerful offboard critic models. They will also mentor and lead a team to push the state-of-the-art in ML-based planning and ensure models meet the highest standards of safety.
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
- 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
- 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 by reaching new frontiers of autonomous driving technology and deploying models on real production vehicles that drive on public roads and must meet the highest standards of safety
- Mentor and lead a team to execute highly technical projects
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
Qualifications
- 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
Skills
- Strong understanding of machine learning algorithms and techniques
- Experience with distributed computing frameworks
- Knowledge of computer vision and robotics
- Experience with simulation and testing frameworks
- Excellent communication and leadership skills
Benefits
- Competitive base salary range of $171K - $247K per year
- Annual bonus
- Equity compensation
- Comprehensive benefits package
Pay
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.
Schedule
Working at Aurora involves a hybrid work environment where employees are in the office at least 3 days per week.
Company Information
Aurora is committed to safety, diversity, and inclusion. We operate in a hybrid work environment and provide reasonable accommodations for qualified individuals with disabilities and disabled veterans. Aurora is an equal opportunity employer and does not discriminate on the basis of race, color, religion, national origin, age, sex, gender, gender identity, gender expression, sexual orientation, marital status, pregnancy status, parent or caregiver status, ancestry, political affiliation, veteran and/or military status, physical or mental disability, or any other status protected by federal or state law.