Senior Machine Learning Engineer, Growth

San Francisco, CAHybridFull TimeSenior$175,000–$215,000 /yrPosted 24 days agoVisa sponsorship available

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Required skills

ML data infrastructure
feature definitions
data leakage
training pipelines
retraining
batch scoring
data integrity
deployment pipelines
production monitoring
feature layer

Nice to have

uplift modeling
causal inference
experimentation
A/B tests
holdouts
ads

Role summary

The Growth Machine Learning Engineering team is seeking a Senior Machine Learning Engineer to partner with Data Science in productionizing ML models. This role will own the ML data infrastructure, focusing on feature pipelines, training data generation, batch inference, and production monitoring. Responsibilities include standardizing feature definitions, eliminating data leakage, automating retraining, and ensuring data integrity. The ideal candidate will have experience building production ML systems, strong SQL and Python skills, and experience with large-scale data processing. Familiarity with feature stores, workflow orchestration, and distributed SQL databases is preferred.

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

Sample Waymo interview questions

1
Design a data ingestion system
system designmedium
2
Design a simulation-based regression detection system for code changes
system designhard
3
Longest Substring with Two Distinct Characters Find the longest substring with at most two distinct characters. Input: s = "ccaabbb" Output: 5 Explanation: The substring "aabbb" contains exactly two distinct characters and has a maximum length of 5.
codingmedium
4
Alien Dictionary Order Derive the alien dictionary order from a sorted list of alien words. Input: words = ["z","x","z"] Output: "" (Empty String) Explanation: The letter 'z' cannot come before 'x' and then suddenly after 'x', creating a cycle and making the dictionary invalid.
codingmedium
5
Palindrome After Deleting One Character Determine if a string can be a palindrome after deleting at most one character. Input: s = "abc" Output: FALSE Explanation: Deleting any single character leaves either "ab", "bc", or "ac", none of which result in a valid palindrome.
codingmedium

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