Waymo
2027 Summer Intern, MS/PhD, Road Understanding, ML Engineer
Mountain View, CaliforniainternshipinternAdded today
About this role
Waymo is seeking MS/PhD interns to develop machine learning models for road understanding in autonomous driving. You'll design neural networks for lane geometry detection and road topology, train pipelines across sensor modalities, and collaborate with research teams on perception challenges.
What you'll do
- Design and implement machine learning models for entity-centric lane geometry detection and topological decoding
- Build and optimize data pipelines and training workflows for multi-modal sensor data
- Create evaluation metrics and benchmark model performance on complex road scenarios
- Debug deep learning architectures and analyze failure cases to improve accuracy
- Collaborate with research mentors and engineering teams on downstream impact analysis
- Document findings and prepare insights for publication or internal deployment
What they're looking for
- Python programming
- Deep learning frameworks (PyTorch, JAX, TensorFlow)
- Computer vision and 3D perception
- Graph neural networks and transformers
- 2D/3D geometry and coordinate transformations
- Spatial and relational reasoning
- Model training and debugging
- Data pipeline construction
Benefits
- Hybrid onsite internship in Mountain View, California
- Mentorship from industry leaders
- Exposure to cutting-edge autonomous driving technology
- Eligible for company benefits programs
- Opportunity to contribute to published research
Opens the official application on the employer’s site. No login required.
Waymo
Waymo develops autonomous driving technology and vehicles, building the AI systems, simulation platforms, and infrastructure that power the Waymo Driver. The company is hiring for ML infrastructure engineers, platform engineers, labeling system developers, backend software engineers, and automotive systems engineers to scale its autonomous driving capabilities.
- Website
- waymo.com
Likely interview questions
- Can you walk us through a computer vision or 3D perception project where you designed and trained a deep learning architecture from scratch?
- How have you approached debugging a neural network that was underperforming on specific edge cases?