Staff Research Scientist, Perception

Title: Staff Research Scientist, Perception

Location: San Francisco United States

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.

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.

This role follows a hybrid work schedule and reports to a Principal Research Scientist.

You will:

• Research & Develop state-of-the-art Multimodal LLMs and World models to perform 3D Perception using sensor information from Camera, LiDAR and Radar.
• Integrate emerging research from the broader AI community into Waymo's Encoders and Sensor understanding models
• Partner with engineering and research teams across Waymo to share recipes, techniques, and best practices to accelerate our collective know-how.
• Develop and maintain scalable data pipelines for Training & Eval to process data from multiple sources.
• Design and implement evaluation frameworks for perception models.
• Study and analyze different behaviors of this model, such as scaling efficacy, downstream quality implications, model architecture design ablations, etc.
• Design and implement Perception Modeling solutions to understand LiDAR/Camera/Radar information from autonomous vehicle sensors.
• Conducting cutting-edge research and potentially communicating research findings to the wider academic community via technical reports and/or publications.

You have:

• PhD or Masters in Computer Science, Machine Learning, Robotics, or a similar technical field, with 4+ years of industry or post-doc research experience in 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 in implementing model training flows in a scalable, distributed and performant manner such as Data parallel, FSDP and other sharding approaches.
• A willingness to work with complexity of globally distributed inference infrastructure.

We prefer:

• PhD in Computer Science, Machine Learning, or Robotics, with a research focus on Reinforcement Learning, Foundation Models, or Multi-Modal learning.
• Extensive experience designing and deploying Reinforcement Learning infrastructure, specifically for on-policy learning or alignment with human preferences.
• A consistent history of original contributions to the AI community, evidenced by first-author publications at top-tier venues (e.g., NeurIPS, ICLR, ICRA) or maintaining significant open-source ML projects
• Substantial involvement in and contributions to high impact industry AI projects.
• Experience in generative models for domains such as world models, images, videos, 3D, using techniques such as diffusion or autoregressive models.

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

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.

Salary Range

$251,000-$310,000 USD

We appreciate your interest in Waymo. Waymo is proud to be an equal opportunity employer, committed to creating a culture of belonging and maintaining a supportive workplace for all employees. We welcome applicants of all backgrounds, and employment decisions are based on a candidate's qualifications, experience, and alignment with job requirements and business needs. Waymo does not discriminate against, and prohibits harassment of, any applicant or employee based on race, color, sex, sexual orientation, gender identity, religion, national origin, age, disability, military status, family status, pregnancy, genetic information or any other basis protected by applicable law. Waymo will also consider for employment qualified applicants with criminal records in accordance with applicable law. Waymo is committed to making sure our hiring process is accessible for all candidates.