ML Delivery Engineer: Growth Modeling & Deployment

Hilbert; United States; Remote; USD 180,000 - 230,000; Full time

Job summary

Hilbert is seeking a senior ML-focused Delivery Engineer to drive end-to-end machine learning projects for customers in diverse environments. You will build feature stores, train and tune models, and validate uplift in production settings, collaborating with data delivery and product engineering teams across time zones.

The role emphasizes model governance, scalable feature pipelines, and practical deployment in customers’ environments or our managed stack, with a focus on measurable growth

Qualifications

• Extensive applied ML experience on tabular customer data.
• Hands-on deep learning experience with PyTorch or similar frameworks.
• Feature engineering in SQL warehouses such as Databricks, Snowflake or BigQuery.
• Production ML using tools like MLflow, Weights & Biases or Vertex AI.
• Solid experiment design and uplift measurement.
• Comfortable working in a distributed team across time zones.

Responsibilities

• Build customer-specific, point-in-time features on the data foundation.
• Train, tune and validate propensity, churn, LTV, uplift and custom models.
• Analyze model results and behavior across segments and edge cases.
• Deploy models in customer environments or on managed stacks; write scores and audiences.
• Own measurement design within the growth team's uplift framework.
• Document models for data science and risk teams; monitor and retrain in production.
• Productize repeated features and model patterns with the product engineering team.

Skills

Python

Deep learning

Experiment design

Remote collaboration

Data science

Tools

MLflow

Weights & Biases

Vertex AI

Databricks

Snowflake

BigQuery

PyTorch

Job description

Hilbert is seeking a senior ML-focused Delivery Engineer to drive end-to-end machine learning projects for customers in diverse environments. You will build feature stores, train and tune models, and validate uplift in production settings, collaborating with data delivery and product engineering teams across time zones.

The role emphasizes model governance, scalable feature pipelines, and practical deployment in customers’ environments or our managed stack, with a focus on measurable growth