Job description

Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states. Software Engineering builds the brains of Waymo's fully autonomous driving technology. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We think deeply and solve complex technical challenges in areas like robotics, perception, decision-making and deep learning, while collaborating with hardware and systems engineers. If you’re a software engineer or researcher who’s curious and passionate about Level 4 autonomous driving, we'd like to meet you. Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship! You will: Analyze and characterize internal feature representations of deep multimodal perception foundation models. Design and implement active learning and intelligent data curation algorithms to identify rare, safety-critical edge cases. Explore automated data labeling and validation workflows leveraging modern Vision-Language Models (VLMs). Train, fine-tune, and evaluate deep neural networks to improve model performance. Validate model performance on large-scale autonomous vehicle sensor datasets and simulation environments. You have: Currently enrolled in a PhD program in Computer Science, Robotics, Electrical Engineering, or a related quantitative field. Experience programming in Python and C++. Practical experience training, fine-tuning, and evaluating deep learning models for computer vision or multimodal perception (e.g., using PyTorch, JAX, or TensorFlow). Solid understanding of modern neural architectures (e.g., Vision Transformers, multi modal sensor encoders). We prefer: Research experience or publications in active learning, self-supervised representation learning, core-set selection, or multi modal foundation models (VLMs). Hands-on experience working with multi-camera or 3D LiDAR perception systems in robotics or autonomous driving. Experience building VLM evaluation/judge pipelines or large-scale data mining workflows. Familiarity with high-dimensional feature analysis and clustering techniques. Note: This will be a hybrid onsite internship position. We will accept resumes on a rolling basis until the role is filled. To be in consideration for multiple roles, you will need to apply to each one individually - please apply to the top 3 roles you are interested in. The expected hourly rate for this full-time position is listed below. Interns are also eligible to participate in the Company’s generous benefits programs, subject to eligibility requirements. Hourly PhD Pay $85 — $85 USD

Requirements

Must-haves

Analyze and characterize internal feature representations of deep multimodal perception foundation models.
Design and implement active learning and intelligent data curation algorithms to identify rare, safety-critical edge cases.
Explore automated data labeling and validation workflows leveraging modern Vision-Language Models (VLMs).
Train, fine-tune, and evaluate deep neural networks to improve model performance.
Validate model performance on large-scale autonomous vehicle sensor datasets and simulation environments.
Currently enrolled in a PhD program in Computer Science, Robotics, Electrical Engineering, or a related quantitative field.
Experience programming in Python and C++.
Practical experience training, fine-tuning, and evaluating deep learning models for computer vision or multimodal perception (e.g., using PyTorch, JAX, or TensorFlow).
Solid understanding of modern neural architectures (e.g., Vision Transformers, multi modal sensor encoders).
Research experience or publications in active learning, self-supervised representation learning, core-set selection, or multi modal foundation models (VLMs).
Hands-on experience working with multi-camera or 3D LiDAR perception systems in robotics or autonomous driving.
Experience building VLM evaluation/judge pipelines or large-scale data mining workflows.
Familiarity with high-dimensional feature analysis and clustering techniques.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

python

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Vision