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About This Role
MOUNTAIN VIEW, CALIFORNIA, UNITED STATES
FULL\-TIME
OPERATIONS
5135
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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.
As an L4 Policy \& Quality Specialist, you will serve as the Mountain View operational backbone of the Labeling Policy Program. You will play a key role in accelerating MTV Perception Engineering velocity by translating complex machine learning data requirements into consistent, high\-quality, and scalable labeling policies. Operating in the same time zone as our core engineering partners, you will drive rapid policy iteration, create critical golden datasets to enable fast labeling queue setup. This is an execution\-focused role for a technical, detail\-oriented specialist who thrives on driving clarity, alignment, and operational excellence in labeling workflows.
You will:
- Translate requirements to policies: Collaborate directly with MTV\-based Perception Engineers/ ML model owners to understand their specific data goals, translate their ambiguous machine learning requirements into precise labeling instructions, and publish clear, actionable labeling policies (with support from the HYD Policy Specialist and vendor partners).
- Drive queue readiness \& golden datasets: Speed up the initial labeling queue setup process by executing rapid policy iterations and hand\-crafting golden datasets (small\-scale baseline datasets of 10s of examples) to establish quality baselines before launching full\-scale operations.
- Direct vendor teams: Provide technical guidance and operational direction to vendor labeling experts to enable rapid policy setup and ensure that the \~10 active labeling queues under your purview run smoothly and meet safety and performance objectives.
- Address edge cases \& regional nuances: Provide critical, detailed inputs on long\-tail edge cases and coordinate with regional country specialists to ensure country\-specific driving rules and local nuances are accurately captured and validated, ahead of Waymo's deployment in these new countries.
- Enable quality and process improvements: Monitor labeling pipelines, conduct targeted technical analyses to identify data quality trends, and build/maintain automated data analysis tools to proactively identify improvements in the broader labeling workflow.
- Facilitate cross\-functional knowledge sharing: Act as the primary technical interface between requesters and operations, ensuring on\-ground dissipation of policies, managing policy amendments, and resolving complex escalations from requestors or vendor teams.
You have:
- 4\-5\+ years of experience in data analysis, operations, or program management with a focus on machine learning data annotation, taxonomy design, or human\-in\-the\-loop workflows.
- Operational project management: Demonstrated ability to work independently on operational workflows and successfully project manage small sub\-working groups or vendor squads.
- Core ML data lifecycle understanding: Practical knowledge of dataset curation, labeling pipelines, data quality control metrics, and baseline model evaluation concepts.
- Analytical aptitude: Experience conducting technical data analyses using pre\-established tools (or building simple automation scripts) to diagnose pipeline issues, track vendor quality, and generate actionable insights.
- Adaptable \& detail\-oriented: Comfort working within a dynamic environment, translating vague technical needs into clear documentation, and maintaining a high standard of attention to detail.
We prefer:
- Experience with scripting languages (e.g., Python, SQL) or basic automation techniques to parse high volumes of critical data.
- Experience collaborating with software engineering stakeholders to gather structured requirements and explain technical operational policies.
- Prior experience working across multiple geographic locations and managing vendor\-hosted operations.
- Familiarity with prompt engineering and evaluation of model outputs (AI\-generated workflows) is a plus.
The expected base salary range for this full\-time position 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. 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
$152,000—$160,000 USD
Salary Context
This $152K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Waymo, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($156K) sits 29% below the category median. Disclosed range: $152K to $160K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Waymo AI Hiring
Waymo has 10 open AI roles right now. They're hiring across AI/ML Engineer. Based in Mountain View, CA, US. Compensation range: $160K - $310K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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