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.

*The Waymo Driver is increasingly powered by state-of-the-art machine learning. The crux of the open challenges remain in the tail end of the scenarios. As we drive millions of RO miles, we need mechanisms to learn from suboptimal driving. With this project, you will be building new levers in the ML models to learn from such data. This involves exploring SOTA fine tuning techniques such as (but not limited to) reinforcement learning. LoRA base finetuning,contrastive learning, etc, improving Waymo driver's abilities to navigate complex real world scenarios. *

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:

Frame the open-ended real-world problems into well-defined ML problems; apply deep learning, reinforcement learning, imitation learning to these problems.

• Develop novel ways to learn from unsupervised driving data. Be able to apply SOTA techniques within Waymo's tooling/infrastructure.

Build data tooling/infrastruction to unblock experimentation.

• Use data-driven decision making to evaluate and compare different approaches

Collaborate closely with partner teams such as perception, research, simulation, and evaluation

You have:

Experience with deep learning concepts and reinforcement learning and reward functions

Proficient in Python and deep learning frameworks

• Proficiency in dealing with large amount of data

Currently pursuing a Masters / PhD degree in Computer Science, Machine Learning, Robotics, or related field

We prefer:

Experience in the autonomous driving domain, including areas like motion planning, perception, or control

• Experience in integrating ML models into complicated systems

Proficient in C++

Proficient in Py Torch, JAX, or Tensor Flow

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 Masters Pay**$70—$70 USD**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**