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Waymo

2027 Summer Intern, PhD, Machine Learning, Computer Vision

Mountain View, California, United States$176.4k–$176.4kinternshipinternAdded today

About this role

Waymo seeks a PhD-level machine learning intern to work on perception foundation models for autonomous driving. You'll analyze deep multimodal models, validate performance on large-scale sensor datasets, and improve data quality through neural network training and data pipeline enhancement.

What you'll do

  • Analyze and characterize internal feature representations of deep multimodal perception foundation models
  • Validate model performance on autonomous vehicle sensor datasets and simulation environments
  • Train, fine-tune, and evaluate deep neural networks to improve perception accuracy
  • Inspect perception models to derive insights on data quality
  • Implement or augment data pipelines to enhance data quality

What they're looking for

  • Python programming
  • Deep learning frameworks (PyTorch, JAX, or TensorFlow)
  • Computer vision and multimodal perception
  • Modern neural architectures (Vision Transformers, sensor encoders)
  • Neural network training and fine-tuning
  • Large-scale dataset handling
  • 3D LiDAR and multi-camera perception systems
  • Model evaluation and validation

Benefits

  • Hybrid onsite internship in Mountain View, California
  • Work on cutting-edge autonomous driving technology
  • Mentorship from industry leaders
  • Participation in company benefits programs
  • Exposure to 100+ million miles of autonomous driving experience
Apply on the employer's site

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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
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Likely interview questions

  • Describe your experience training and fine-tuning deep learning models for computer vision—what frameworks have you used and what were the results?
  • How have you worked with multimodal sensor data (cameras, LiDAR, etc.) in robotics or autonomous driving projects?