- Location
- Mountain View, CA; San Francisco, CA; Kirkland, WA · Kirkland (US-KIR-6THD) · Mountain View (US-MTV-EMF680) · San Francisco (US-SFO-MKT555)
- Workplace
- Hybrid
- Department
- Education
- Seniority
- Senior
- Education
- PhD
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.
The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.
In this hybrid role, you will report to a Senior Director of AI Foundations
You will:
- Own the data recipe for Waymo’s Foundation Model pre-training and post-training
- Lead and drive science on best practices around clustering, filtering, de-duplication, long-tail data mining, memorization, etc.
- Tech-lead a team of research engineers to build the data flywheel to enable the above from Waymo’s massive driving data
- Integrate emerging research from the broader community to do rigorous ablations and promising data research techniques. This includes scaling ladders for pre-training, data mix optimizations and RL recipes (preferences) for post-training.
- Engage with the wider research community on best practices around data centric evaluation creation and refinement
- Partner with engineering and research teams across Waymo to share recipes, techniques, and post-training best practices to accelerate our collective know-how
You have:
- PhD or Masters in Computer Science, Machine Learning, Robotics, or a similar technical field; with 3+ years of industry or post-doc research experience in Data-centric AI, Reinforcement Learning or Foundation Models
- Demonstration of original contributions to the field through high-impact publications (ArXiv, peer-reviewed conferences like NeurIPS/ICLR/CVPR), technical blog posts, or significant open-source contributions
- Proficiency and in-depth knowledge of the inner workings of an ML framework (e.g. Pytorch, JAX, Tensorflow)
We prefer:
- Extensive experience working with data and recipes for large scale foundation models
- ML infra experience: training, evaluating and deploying ML models at scale
- Deep learning experience, especially with generative models, e.g., LLMs/VLMs, and/or reinforcement learning
In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:
- Health, dental, vision, life, disability insurance
- Retirement Benefits: 401(k) with company match
- Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment
- Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary)
- Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks
- Baby Bonding Leave: 18 weeks
- Holidays: 13 paid days per year
Please note that Waymo may not be able to employ remotely in all locations. Please speak with your recruiter about your preferred location for remote work when you begin the interview process
The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.
Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.