Waymo
2027 Summer Intern, MS/PhD, Perception, Machine Learning
About this role
Waymo is seeking MS/PhD interns for summer 2027 to develop cutting-edge perception systems for autonomous vehicles. You'll design and implement multi-modal ML models (camera, radar, lidar, audio) for tasks like 3D object detection, tracking, and occupancy detection using vast real-world driving data and large-scale infrastructure.
What you'll do
- Design and implement multi-modality perception models for autonomous driving
- Develop state-of-the-art solutions for 3D object detection, tracking, and segmentation
- Train and evaluate ML models on large-scale Waymo driving logs
- Work on foundation models and self-supervised learning through forecasting
- Collaborate with downstream teams on model optimization and integration
- Gain hands-on experience with large-scale ML infrastructure
What they're looking for
- Python programming
- PyTorch, JAX, or TensorFlow
- Machine learning model development
- Computer vision and perception
- 3D object detection or tracking
- Multi-task learning
- Self-supervised learning
- Large-scale data processing
Benefits
- Access to millions of miles of diverse sensor data
- Large-scale ML infrastructure and computing resources
- Mentorship from industry leaders
- Opportunity to work on real-world autonomous driving problems
- Participation in company benefits programs
- On-site hybrid position at Mountain View headquarters
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 one of your published papers or research projects in computer vision or machine learning?
- Describe your experience with multi-modal learning across different sensor types like cameras, lidar, and radar.