Staff Machine Learning Engineer – VLM/LLM

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 you will report to a Senior Staff Software Engineer.

You will:

• Work with a creative team of people who help to build the state-of-the-art Foundation Models that are used throughout Waymo’s systems, both onboard autonomous vehicles and offboard in simulation

• Lead the development of end-to-end evaluation systems and benchmarks for Waymo Foundation models, encompassing the entire lifecycle from pretraining and supervised fine-tuning (SFT) to reinforcement learning (RL), for evaluating the quality, safety, and realism of embodied AI agents

• Partner within and across organizations to land disruptive and innovative tech in production

• Implement and extend large large scale data and evaluation pipelines

You have:

• Master’s degree or PhD degree in Computer Science, similar technical field of study, or equivalent practical experience

• 5+ years of experience in ML engineering and applied Deep Learning, with a strong portfolio of shipped products or publication record

• Experience with large scale distributed system

• Proficient programming skills (eg: Python, C/C++)

• Strong analytical and debugging skills

We prefer:

• 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

• Proficiency and in-depth knowledge of the inner workings of an ML framework (e.g. Pytorch, JAX, Tensorflow)

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

$238,000—$302,000 USD