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 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. Salary Range $175,000 — $215,000 USD

Requirements

Must-haves

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.
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.
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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningmlgosqlpython

Platform: greenhouse
Effort: quick