Senior Machine Learning Engineer, Growth

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.