Autonomy ML & Planning Intern

Aurora; San Francisco (CA); On-site; USD 72,000 - 90,000; Full time

Job summary

Aurora is seeking Summer 2027 Behavior & Planning Interns to contribute to how the Aurora Driver forecasts, plans, and learns from data. You will explore ML models for motion planning and prediction, under mentorship, on real-world datasets, with an emphasis on safety and robustness.

Join a team that tackles complex autonomy challenges and collaborates across ML, robotics, and software, with compensation based on degree and location (SF/MBay or Pittsburgh).

Qualifications

• Pursuing a Bachelors, Masters or PhD in Computer Science, Robotics, or a related field, with a research or ML focus.
• Strong ML foundations with hands-on experience in PyTorch, including custom dataloaders, architectures, and loss functions.
• Exposure to imitation learning, reinforcement learning, or motion planning.
• Experience training models on large-scale, non-toy datasets or benchmarks.
• Ability to reason mathematically about optimization objectives.
• Awareness of the compounding-error / distribution-shift problem in imitation learning.

Responsibilities

• Develop end-to-end planning or prediction models for the Aurora Driver using imitation learning, reinforcement learning, and/or classical motion planning.
• Formulate ambiguous driving-behavior problems as concrete ML or optimization problems.
• Reason about competing objectives (e.g. progress vs. safety) and uncertainty in your model design and evaluation.
• Work with autonomy/robotics-scale datasets and benchmarks, not just toy datasets.
• Present a project plan and regular progress updates to the team, working closely with a mentor.

Skills

PyTorch experience

Imitation learning

Reinforcement learning

Large-scale datasets

Optimization reasoning

Education

Bachelor/Master/PhD in CS/Robotics or related field

Job description

Aurora is seeking Summer 2027 Behavior & Planning Interns to contribute to how the Aurora Driver forecasts, plans, and learns from data. You will explore ML models for motion planning and prediction, under mentorship, on real-world datasets, with an emphasis on safety and robustness.

Join a team that tackles complex autonomy challenges and collaborates across ML, robotics, and software, with compensation based on degree and location (SF/MBay or Pittsburgh).