Staff Machine Learning Engineer

Evolve

20h•$180,000 - $195,000•Remote

About The Position

Evolve's Data Product team builds data and machine-learning products that improve owner acquisition and retention, pricing, revenue performance, and how teams make decisions. We're hiring a hands-on Staff Machine Learning Engineer with applied data science depth to build and own the highest-impact ML systems across that portfolio. You'll turn ambiguous opportunities into durable production systems: frame the problem and success measures, develop and validate models, build the pipelines and inference patterns needed to run them, integrate them into products or workflows, and measure adoption and business impact. You'll also contribute directly to our economic and pricing work, including forecasting, demand and price-elasticity estimation, causal measurement, and optimization. This is an individual-contributor role for someone who enjoys both modeling and systems work. You'll set technical direction and pragmatic engineering standards for a lean data science team, mentor other contributors, and partner closely with Product, Engineering, Data Engineering, Revenue Management, and business leaders. The exact mix of initiatives will shift with company priorities, but end-to-end ownership and measurable business use will remain constant. This role reports to the head of Data Product.

Requirements

Typically 8+ years applying machine learning, data science, statistics, or econometrics to consequential business problems.
Evidence of Staff-level scope: you have led ambiguous, cross-functional ML or DS work from idea through sustained production use, influenced technical direction across multiple teams or at least 2–3 significant projects, and created durable technical leverage for other contributors.
Expert-level Python and SQL skills, including the ability to design, write, test, and maintain production-quality code while working effectively with large and complex datasets.
Expert-level applied modeling and model-evaluation judgment, including selecting appropriate modeling approaches, assessing tradeoffs and limitations, validating model performance, and determining when simpler or more sophisticated methods are appropriate.
Experience owning production ML systems end to end, including deployment or scoring, monitoring, failure handling, operational support, and iteration after launch.
Hands-on experience with modern data and ML infrastructure such as Snowflake, dbt, cloud orchestration, and practical MLOps or model-governance patterns, or comparable technologies and architectures.
Strong applied-statistics judgment, including experimentation or causal-inference fundamentals and the ability to contribute meaningfully to economic, forecasting, or optimization work.
Comfortable working in Git-based development environments with strong software-engineering practices, including code review, automated testing, CI/CD, versioning, and safe production deployment.
Ability to connect technical decisions to business outcomes and communicate tradeoffs clearly to technical, product, business, and executive audiences.
A self-directed, collaborative working style: you can prioritize across a broad problem space, receive and give direct feedback, influence without formal authority, and remain hands-on in a lean, remote team.

Nice To Haves

An advanced degree in a quantitative field, or equivalent depth demonstrated through applied work.
Demand forecasting, dynamic pricing, revenue management, price-elasticity estimation, econometrics, causal inference, or optimization.
Marketplace, travel, hospitality, or another domain where models influence high-frequency operating decisions.
NLP, LLM, voice, or other unstructured-data products.

Responsibilities

Lead the technical direction and hands-on delivery of one or two prioritized applied ML initiatives at a time.
Translate business opportunities into clear decision frameworks, technical approaches, success measures, and plans for adoption and impact evaluation.
Build and operate ML systems end to end, including data and feature pipelines, training and evaluation, batch or online inference, deployment, monitoring, failure handling, and iteration.
Contribute directly to forecasting, demand and price-elasticity estimation, causal measurement, and optimization work that supports pricing and revenue decisions.
Design and analyze experiments and quasi-experiments to evaluate product, model, and policy changes.
Establish reusable standards for testing, reproducibility, versioning, observability, documentation, and responsible model operation; review designs and code across the team.
Partner with Product, Engineering, Data Engineering, Revenue Management, and other business owners to make technical tradeoffs and integrate models into products and operating workflows.
Mentor data scientists, raise the team's technical judgment, and create leverage beyond your own projects while remaining hands-on.