Senior Research Scientist, Perception

Waymo; Kirkland (WA); Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Health, dental, vision, life, and disability insurance

401(k) with company match

20 days of vacation per year

Maternity leave of 28–30 weeks

13 paid holidays per year

Job summary

Waymo, located in Kirkland, Washington, is seeking a talented researcher to focus on advanced machine learning solutions for autonomous driving. This role includes responsibilities such as developing multimodal LLMs for 3D perception and integrating cutting-edge research into existing models.

Candidates should have a Ph.D. or master’s in a relevant field and possess significant research experience. Waymo offers a hybrid work schedule along with comprehensive benefits, including health insurance and retirement plans.

Qualifications

• 2+ years of industry or post-doc research experience in reinforcement learning or foundation models.
• High-impact publications in conferences such as NeurIPS/ICLR/CVPR.
• Proficiency in implementing scalable model training flows.

Responsibilities

• Research & develop multimodal LLMs and world models for 3D perception.
• Integrate research into Waymo's models and encoders.
• Design and implement perception modeling solutions.

Skills

Reinforcement learning

Machine learning

3D perception

Data pipelines

Education

Ph.D. or master’s in computer science or machine learning

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.

Role Overview

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

Responsibilities

• 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.
• Develop and maintain scalable data pipelines for training and evaluation to process data from multiple sources.
• Design and implement evaluation frameworks for perception models.
• Study and analyze different behaviors of the 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.
• Conduct cutting‑edge research and potentially communicate research findings to the wider academic community via technical reports and/or publications.

Qualifications

• Ph.D. or master’s in computer science, machine learning, robotics, or a similar technical field, with 2+ 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 the complexity of globally distributed inference infrastructure.

Preferred Qualifications

• Ph.D. in computer science, machine learning, or robotics, with a research focus on reinforcement learning, foundation models, or multimodal 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 such as 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.

Benefits

• Health, dental, vision, life, and 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 during the first five years of employment.
• Sick time: 40 hours per year (statutory, where applicable); 5 days per event (discretionary).
• Maternity leave (short‑term disability + baby bonding): 28–30 weeks.
• Baby bonding leave: 18 weeks.
• Holidays: 13 paid days per year.