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.
Rigorous performance evaluation of the Waymo Driver is a critical part of scaling our ride hailing service and achieving Waymo’s audacious goals. Waymo data scientists work hand-in-hand with engineering teams at each stage of the software development cycle, employing statistical models and developing metrics and measurement frameworks to ensure that the Waymo Driver meets our strict standards for safety, compliance, and driving and service quality. Autonomous driving with complex machine learning systems presents a new paradigm in data science: in addition to leveraging data collected on-road, we generate our own data using state-of-the-art simulation technology—resulting in denser signals and challenging new problems in estimation and experimental design.
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:
Create innovative statistical and machine learning methods and practical guidelines for assessing early software versions, prioritizing actionable insights and efficient evaluation
Develop tools that pinpoint meaningful driving behavior changes resulting from even minor code modifications to major ML model updates, effectively guiding engineer’s focus to the signals among noise
Develop and optimize sim-based eval strategies for a high priority novel problem area
Validate your methodology and provide code to enable ongoing use of your methodology (by leveraging the Alphabet software development infrastructure)
Present findings to stakeholders
You have:
Pursuing a PhD in a quantitative field (e.g. Statistics, Mathematics, Physics, Engineering, Economics, Political Science)
A solid statistical theory foundation
Excellent data institution- you are able to quickly grasp data challenges, understand the statistical nuances, propose a strategy to solve those challenges, and implement a solution
Proficiency in programming with Python with an emphasis on statistical coding, data manipulation, and data visualization; also basic knowledge of SQL
Proven skills in effective communication through presentations and written documentation
Excellent interpersonal skills
We prefer:
Experience with developing statistical methodology, casual inference, multiple hypothesis testing, A/B experiments, sampling methods, and/or predictive modeling
Experience with interpretation and evaluation of complex ML systems and/or foundation models
3+ years of experience in data wrangling, visualization and data-driven storytelling
Fluency in SQL
Industry experience
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
$60.10—$60.10 USD
