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