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 Growth Machine Learning Engineering team partners closely with Data Science to take models from experimentation into reliable production. We own the underlying ML data infrastructure: standardizing shared feature definitions, eliminating data leakage in training pipelines, automating scheduled retraining, and establishing safeguards so faulty batch scoring runs never affect live promotions. By managing data integrity, deployment pipelines, and production monitoring, we provide the dedicated engineering support needed to keep model-driven growth initiatives dependable and scalable.
You will:
- Own the feature layer between the Growth DB and the models. Work with data engineering on schema design for ML features, and with data science to turn their feature lists into versioned feature definitions that training and scoring both read from.
- Build point-in-time correct training data, so a retrain sees each day as it was and does not leak future data. Data science has already named this as a gap in today's pipelines.
- Take models data science has trained and put them into production: validate against the offline results, wire them into batch scoring, and add input and output checks that stop a bad run before promos go out.
- Lead training automation: scheduled retrains, a model registry, and validation before a new model version is promoted.
- Monitor models in production: feature drift, score distribution shifts, and model versus random arm results.
- Be data science's day-to-day engineering counterpart, so feature and modeling questions do not queue behind the tech lead.
You have:
- Experience at roughly building production ML systems: feature pipelines, training data generation, batch inference.
- Strong SQL and Python, plus large-scale data processing (Flume, Beam, Spark or similar).
- Designed feature schemas or worked on a feature store, and understands point-in-time correctness and train/serve skew from having fixed them.
- Productionized and owned the lifecycle of an existing offline model
- Worked with data scientists and can turn a loose feature request into a spec.
We prefer:
- Background in uplift modeling, causal inference or experimentation (A/B tests, holdouts), enough to understand what the models need.
- Experience in ads, marketing, recommendations, pricing or promotions.
- Familiarity with feature stores, workflow orchestration (Airflow or similar) and distributed SQL databases.
#LI-Hybrid
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.
Skills Required
- Experience building production machine learning systems, including feature pipelines, training data generation, and batch inference
- Strong SQL and Python skills
- Experience with large-scale data processing such as Flume, Beam, Spark, or similar
- Experience designing feature schemas or working with a feature store
- Understanding of point-in-time correctness and train-serve skew, including experience fixing these issues
- Experience productionizing and owning the lifecycle of an existing offline model
- Experience collaborating with data scientists and translating feature requests into specifications
- Background in uplift modeling, causal inference, or experimentation
- Experience in ads, marketing, recommendations, pricing, or promotions
- Familiarity with feature stores and workflow orchestration such as Airflow
- Familiarity with distributed SQL databases
Waymo Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Waymo and has not been reviewed or approved by Waymo.
-
Fair & Transparent Compensation — Pay is considered strong for full‑time technical roles, aligning with top‑tier AV/Big Tech benchmarks. Employer materials also emphasize competitive pay with eligibility for bonuses and equity as part of total rewards.
-
Healthcare Strength — Coverage spans medical, dental, vision, mental‑health resources, onsite wellness centers, and counseling, with specialized programs such as menopause and transgender medical advocacy. Dependents are included, indicating depth across core health needs.
-
Parental & Family Support — Offerings include fertility and family‑forming assistance, parental and baby‑bonding leave, caregiver and elder‑care support, and backup childcare. These programs are positioned to support families through multiple life stages.
Waymo Insights
What We Do
Waymo is an autonomous driving technology company with a mission to make it safe and easy for people and things to move around. With the Waymo Driver, we can improve the world’s mobility while saving thousands of lives. Waymo reaches out to candidates from official channels only (e.g. directly from @waymo.com email addresses, or through our recruiters or sourcers who are noted as such on LinkedIn). We do not contact candidates about career opportunities through instant messaging apps like Telegram, email addresses from domains other than waymo.com (such as Gmail addresses), direct messages on Twitter, Facebook, and Instagram, or text messages. Visit waymo.com to check out our official job listings.
