6 hours ago
Mountain View, CA, USAIntern
H1B sponsor
Responsibilities
- Analyze and characterize internal feature representations of deep multimodal perception foundation models.
- Validate model performance on large-scale autonomous vehicle sensor datasets and simulation environments.
- Train, fine-tune, and evaluate deep neural networks for improved model performance.
- Inspect perception foundation models to identify signals related to data quality.
- Implement or augment data pipelines to improve data quality.
Requirements
- Currently enrolled in a PhD program in computer science, robotics, electrical engineering, or a related quantitative field.
- Experience programming in Python.
- Practical experience training, fine-tuning, and evaluating deep learning models for computer vision or multimodal perception using technologies such as PyTorch, JAX, or TensorFlow.
- Solid understanding of modern neural architectures such as Vision Transformers and multimodal sensor encoders.
- Preferred: research experience or publications in multimodal deep learning or vision foundation models.
- Preferred: hands-on experience with multi-camera or 3D LiDAR perception systems in robotics or autonomous driving.
- Preferred: experience evaluating large-scale perception systems in practical settings.
Benefits
- Hybrid onsite work arrangement.
- Full-time summer internship with applications accepted on a rolling basis until the role is filled.
- Interns are eligible for Waymo’s benefits programs subject to eligibility requirements.
- Hourly PhD pay of $85.
Categories
ML EngineeringRobotics
About Waymo
Waymo develops the Waymo Driver, a full-stack autonomous driving system, and operates the Waymo One robotaxi service that offers paid, driverless rides in select U.S. cities. The company monetizes through ride-hailing fares and partnerships integrating its technology on multiple vehicle platforms. Founded in 2009 as Google’s self-driving project, Waymo is headquartered in Mountain View and is an Alphabet subsidiary, with over 10 million rider-only trips and 100+ million autonomous miles on public roads.