Waymo
2027 Summer Intern, PhD, Machine Learning, Computer Vision
Mountain View, California$176.4k–$176.4kinternshipinternAdded yesterday
About this role
Waymo seeks a PhD-level machine learning intern to advance autonomous driving perception by analyzing deep neural network representations, designing data curation algorithms, and training multimodal vision models on large-scale sensor datasets.
What you'll do
- Analyze feature representations in deep multimodal perception foundation models
- Design and implement active learning algorithms to identify safety-critical edge cases
- Develop automated data labeling and validation workflows using Vision-Language Models
- Train, fine-tune, and evaluate deep neural networks for perception tasks
- Validate model performance on autonomous vehicle sensor data and simulation environments
What they're looking for
- Python and C++ programming
- Deep learning frameworks (PyTorch, JAX, or TensorFlow)
- Computer vision and multimodal perception
- Modern neural architectures (Vision Transformers, sensor encoders)
- Active learning and data curation techniques
- Vision-Language Models and evaluation pipelines
- High-dimensional feature analysis and clustering
- Multi-camera and LiDAR perception systems
Benefits
- Opportunity to work on Level 4 autonomous driving technology
- Mentorship from industry leaders and experienced engineers
- Access to large-scale autonomous vehicle datasets and simulation
- Exposure to cutting-edge perception and machine learning research
- Participation in company benefits programs
- Hybrid work arrangement in Mountain View
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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
- Walk us through a project where you trained and evaluated a deep learning model for computer vision—what challenges did you face and how did you address them?
- How have you applied active learning or data curation strategies in your research, and what were the results?