PhD Machine Learning Intern - Autonomous Driving Research

Waymo; Mountain View (CA); On-site; USD 99,000 - 135,000; Full time

Job summary

Waymo is seeking a PhD‑level software research intern to contribute to perception and multimodal data processing for autonomous driving. You will analyze internal representations, develop active learning pipelines, and improve model training and evaluation using PyTorch, JAX, or TensorFlow.

The role targets PhD students, offering exposure to real‑world autonomous driving data, large scale simulations, and cutting‑edge VLM concepts in a collaborative research environment.

Qualifications

• Enrolled in a PhD program in Computer Science, Robotics, Electrical Engineering, or related field.
• Proficient in Python and C++.
• Experience training, fine-tuning, and evaluating DL models for vision or multimodal perception (PyTorch/JAX/TensorFlow).
• Solid understanding of modern neural architectures (Vision Transformers, multimodal encoders).

Responsibilities

• Analyze and characterize internal feature representations of deep multimodal perception foundation models.
• Design and implement active learning and data curation algorithms for rare, safety-critical edge cases.
• Explore automated data labeling and validation workflows using modern Vision‑Language Models.
• Train, fine-tune, and evaluate deep neural networks to improve model performance.
• Validate model performance on large-scale AV sensor datasets and simulation environments.

Skills

Python

C++

PyTorch

JAX

TensorFlow

Vision Transformers

Multimodal perception

Education

PhD in Computer Science/Robotics/EE

Tools

PyTorch

TensorFlow

JAX

Job description

Waymo is seeking a PhD‑level software research intern to contribute to perception and multimodal data processing for autonomous driving. You will analyze internal representations, develop active learning pipelines, and improve model training and evaluation using PyTorch, JAX, or TensorFlow.

The role targets PhD students, offering exposure to real‑world autonomous driving data, large scale simulations, and cutting‑edge VLM concepts in a collaborative research environment.