ML Engineer Jobs at Waymo with Visa Sponsorship
ML Engineer jobs at Waymo sit at the intersection of perception, prediction, and real-world autonomy, requiring deep expertise in areas like sensor fusion, large-scale model training, and safety-critical systems. Waymo has a strong track record of sponsoring work visas for ML Engineers across multiple visa categories.
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Get Access To All JobsINTRODUCTION
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 DUE Machine Learning team will build and operate scalable machine learning and data systems, simulation workflow and insight tools, improve and speed up the evaluation and onboard developer journeys. It will combine expert human judgments and advanced machine learning models to deliver training and evaluation data for hundreds of metrics and components that make up the Waymo Driver. We are looking for researchers and software engineers who are passionate about developing machine learning techniques. These techniques are for the Evaluation systems on our autonomous service. They will serve as a constant driver to improve the performance of our technology stack.
ROLE AND RESPONSIBILITIES
You will:
- Grow the end-to-end strategy for our next generation of machine learning-based evaluation metrics, promoting scientific and statistical rigor across our embodied AI applications
- Architect and build scalable systems for training and fine-tuning large-scale generative models to produce realistic and evaluate interesting driving behaviors
- Lead the design, implementation, and iteration of novel RL algorithms, reward functions, and training paradigms tailored for generating high-fidelity and insightful driving behaviors
- Lead the development of cutting-edge Deep Learning models and Generative AI (LLM/VLM) solutions. These solutions will enhance human-led triaging, introduce automation for high-volume workflows, and perform nuanced analysis of self-driving behavior to detect critical anomalies
- Proactively monitor and assimilate best practices from within Alphabet and the broader industry to develop a novel Reinforcement Learning from Human Preference (RLHF) based data collection and evaluation system
- Provide technical mentorship, guidance, and thought leadership to other engineers within the team and across collaborating groups
- Guide and align multiple teams—including Driver Understanding, Simulation, System Engineering, Research, and Onboard Software—on a cohesive evaluation strategy, ensuring cross-functional alignment on goals and priorities
BASIC QUALIFICATIONS
You have:
- PhD degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience
- 7+ years of hands-on experience in developing and applying Machine Learning models, with a significant focus on Reinforcement Learning
- 2+ years of people management experience
- Demonstrated expertise in deep learning, sequence modeling, and generative models
- Strong publication record or history of impactful project delivery in RL or related areas
- Proficiency in Python and standard ML frameworks (e.g., JAX, TensorFlow)
- Experience with large-scale distributed training and data processing
- Proven ability to lead complex and ambiguous technical projects from conception to completion
PREFERRED QUALIFICATIONS
We prefer:
- 10+ years of relevant experience in ML/RL research and application
- Experience in the autonomous vehicles domain, robotics, or complex simulation environments
- Deep understanding of state-of-the-art RL techniques, including those used for fine-tuning large models (e.g., from human feedback/preferences)
- Familiarity with large-scale simulation platforms and their integration with ML training workflows
- Experience designing and using metrics for evaluating complex AI systems
- Track record of technical leadership, influencing senior stakeholders, and driving innovation across team boundaries
- Excellent communication skills, with the ability to articulate complex technical concepts clearly
COMPENSATION
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.
- Salary Range: $281,000—$356,000 USD
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.
INTRODUCTION
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 DUE Machine Learning team will build and operate scalable machine learning and data systems, simulation workflow and insight tools, improve and speed up the evaluation and onboard developer journeys. It will combine expert human judgments and advanced machine learning models to deliver training and evaluation data for hundreds of metrics and components that make up the Waymo Driver. We are looking for researchers and software engineers who are passionate about developing machine learning techniques. These techniques are for the Evaluation systems on our autonomous service. They will serve as a constant driver to improve the performance of our technology stack.
ROLE AND RESPONSIBILITIES
You will:
- Grow the end-to-end strategy for our next generation of machine learning-based evaluation metrics, promoting scientific and statistical rigor across our embodied AI applications
- Architect and build scalable systems for training and fine-tuning large-scale generative models to produce realistic and evaluate interesting driving behaviors
- Lead the design, implementation, and iteration of novel RL algorithms, reward functions, and training paradigms tailored for generating high-fidelity and insightful driving behaviors
- Lead the development of cutting-edge Deep Learning models and Generative AI (LLM/VLM) solutions. These solutions will enhance human-led triaging, introduce automation for high-volume workflows, and perform nuanced analysis of self-driving behavior to detect critical anomalies
- Proactively monitor and assimilate best practices from within Alphabet and the broader industry to develop a novel Reinforcement Learning from Human Preference (RLHF) based data collection and evaluation system
- Provide technical mentorship, guidance, and thought leadership to other engineers within the team and across collaborating groups
- Guide and align multiple teams—including Driver Understanding, Simulation, System Engineering, Research, and Onboard Software—on a cohesive evaluation strategy, ensuring cross-functional alignment on goals and priorities
BASIC QUALIFICATIONS
You have:
- PhD degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical experience
- 7+ years of hands-on experience in developing and applying Machine Learning models, with a significant focus on Reinforcement Learning
- 2+ years of people management experience
- Demonstrated expertise in deep learning, sequence modeling, and generative models
- Strong publication record or history of impactful project delivery in RL or related areas
- Proficiency in Python and standard ML frameworks (e.g., JAX, TensorFlow)
- Experience with large-scale distributed training and data processing
- Proven ability to lead complex and ambiguous technical projects from conception to completion
PREFERRED QUALIFICATIONS
We prefer:
- 10+ years of relevant experience in ML/RL research and application
- Experience in the autonomous vehicles domain, robotics, or complex simulation environments
- Deep understanding of state-of-the-art RL techniques, including those used for fine-tuning large models (e.g., from human feedback/preferences)
- Familiarity with large-scale simulation platforms and their integration with ML training workflows
- Experience designing and using metrics for evaluating complex AI systems
- Track record of technical leadership, influencing senior stakeholders, and driving innovation across team boundaries
- Excellent communication skills, with the ability to articulate complex technical concepts clearly
COMPENSATION
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.
- Salary Range: $281,000—$356,000 USD
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.
Tips for Finding ML Engineer Jobs at Waymo
Tailor your portfolio to autonomy-specific ML
Waymo's ML Engineering roles emphasize perception pipelines, motion forecasting, and on-device inference. Frame your projects around safety-critical or real-time ML systems rather than general deep learning work to stand out in early resume screens.
Confirm your visa category before applying
Waymo sponsors H-1B, H-1B1 visa, and E-3 visas for ML Engineers. If you're Australian or a Singapore national, the H-1B1 and E-3 categories skip the lottery entirely, which significantly changes your sponsorship timeline and certainty.
Use Migrate Mate to filter open ML Engineer roles
Waymo posts ML Engineer openings across specializations like simulation, prediction, and mapping. Use Migrate Mate to filter specifically for Waymo roles that list visa sponsorship, so you're applying to positions where sponsorship is already confirmed.
Prepare for a multi-stage technical interview process
Waymo's ML Engineer interviews typically include a coding screen, a machine learning systems design round, and domain-specific technical interviews. Practicing ML system design at the scale of autonomous vehicle pipelines gives you a concrete edge.
Request premium processing before your start date
Once you have an offer, ask your Waymo recruiter early whether premium processing will be used for your H-1B petition. USCIS standard processing can take months. Premium processing reduces the adjudication window to 15 business days and protects your start date.
Document your specialized experience for your LCA
Your employer files a Labor Condition Application with the DOL before submitting your H-1B petition. For ML Engineer roles, having clear documentation of specialized work in areas like neural network architecture or reinforcement learning strengthens the specialty occupation classification.
Frequently Asked Questions
Does Waymo sponsor H-1B visas for ML Engineers?
Yes, Waymo sponsors H-1B visas for ML Engineers and has a consistent track record of doing so across its engineering organization. Sponsorship typically covers the full H-1B petition process, including the Labor Condition Application filed with the DOL. If you're subject to the H-1B lottery, your petition must be selected before USCIS can adjudicate it, so timing your application cycle matters.
Which visa types does Waymo commonly use for ML Engineers?
Waymo sponsors H-1B, H-1B1 visa, E-3 visa, and Green Card pathways including EB-2 and EB-3 for ML Engineers. Australian citizens can use the E-3 visa, which has its own annual allocation and no lottery. Singapore nationals may qualify for the H-1B1. Both are often faster paths to starting work than the standard H-1B cap process.
How do I apply for ML Engineer jobs at Waymo?
Applications go through Waymo's careers portal. ML Engineer roles are organized by specialization, including areas like perception, prediction, mapping, and simulation, so search by subdomain rather than just the job title. You can also browse current Waymo ML Engineer openings that confirm visa sponsorship on Migrate Mate, which filters roles specifically for candidates who need sponsorship.
What qualifications and experience does Waymo expect for ML Engineer roles?
Most Waymo ML Engineer positions require a graduate degree in computer science, electrical engineering, or a related field, along with hands-on experience training and deploying large-scale models. Practical experience with sensor data, real-time inference, or safety-critical ML systems is weighted heavily. Strong publication records or open-source contributions in relevant areas can differentiate candidates in competitive roles.
How do I understand the visa sponsorship timeline for a Waymo ML Engineer offer?
Once Waymo extends an offer, the sponsorship process begins with a DOL Labor Condition Application, followed by the USCIS H-1B petition. Standard processing can take three to six months. If you're on OPT, your 60-day grace period and OPT expiration date directly affect whether a cap-gap or premium processing strategy is needed. Clarify these timelines with your recruiter before signing.