Staff Machine Learning Engineer

Why this role

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 Staff Machine Learning Engineer to own how those models get to production and stay there: the training and inference systems, the deployment and monitoring patterns, and the engineering standards the rest of the team builds on.

You'll be Evolve's first and most senior ML engineer. You'll turn working models into durable production systems: design feature and training pipelines, batch and online inference, deployment, monitoring, and retraining; integrate models into products and operating workflows; and measure adoption and business impact. You'll be a key decision-maker on ML infrastructure strategy, choosing what we build, what we buy, and what we standardize on. You'll partner with data scientists on the modeling itself, including forecasting, price-elasticity, and optimization work for pricing and revenue, and on LLM-based products.

This is a hands-on individual-contributor role for someone who wants to set technical direction, not just execute it. You'll define pragmatic engineering standards for a lean data science team, mentor engineers and data scientists, and partner closely with Product, Engineering, Data Engineering, Revenue Management, and business leaders. The mix of initiatives will shift with company priorities; end-to-end ownership and measurable business use will not. This role reports to Anto Kajajian.

What you'll do

• Build and operate ML systems end to end: data and feature pipelines, training and evaluation, batch or online inference, deployment, monitoring, retraining, failure handling, and iteration after launch.
• Define how ML ships at Evolve: the patterns for packaging, deploying, versioning, monitoring, and retraining models on Snowflake, dbt, Python, AWS, and our Kubernetes-based deployment stack, plus the CI/CD, logging, and observability standards (GitHub, Sumo Logic) the team builds on.
• Shape ML infrastructure strategy as a key decision-maker: evaluate build-versus-buy options, choose platform components, and set the roadmap for how the team's production footprint grows.
• Productionize LLM-based products: evaluation harnesses, guardrails, cost and latency management, and the operating patterns that keep them reliable after launch.
• Lead the technical direction and hands-on delivery of one or two prioritized ML initiatives at a time.
• Translate business opportunities into clear technical approaches, success measures, and plans for adoption and impact evaluation.
• Partner with data scientists on forecasting, price-elasticity, and optimization models for pricing and revenue decisions, owning the path from prototype to production and instrumenting the experiments that measure impact.
• 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 engineers and data scientists, raise the team's engineering judgment, and create leverage beyond your own projects while remaining hands-on.

What makes you a great fit

• Typically 8+ years building and operating production ML or data-intensive systems, with several of those years owning systems that business decisions depended on.
• Evidence of Staff-level scope: you have led ML systems from idea through sustained production use, set technical direction beyond one project, and created leverage for other engineers and data scientists.
• Expert Python and strong software engineering fundamentals: testing, code review, CI/CD, packaging, dependency management, and observability. Strong SQL and comfort working with large datasets in a cloud warehouse.
• Experience designing and running production ML systems: training pipelines, batch or online inference, deployment, monitoring, retraining, incident response, and iteration after launch.
• Hands-on experience with cloud infrastructure (AWS or equivalent), containers and orchestration (Kubernetes or equivalent), and the tradeoffs involved in choosing and operating ML platform components.
• Enough modeling and evaluation judgment to be a real partner to data scientists: offline and online evaluation, leakage, drift, and basic experiment design.
• 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.

Helpful experience

• Snowflake, dbt, feature stores, model registries, workflow orchestration, and practical MLOps or model-governance patterns.
• LLM evaluation, retrieval-augmented generation, agents, or cost and latency optimization for LLM products in production.
• Demand forecasting, dynamic pricing, revenue management, price-elasticity estimation, causal inference, or optimization.
• Marketplace, travel, hospitality, or another domain where models influence high-frequency operating decisions.
• Standing up ML infrastructure or platform patterns at a company that did not have them yet.
• An advanced degree in a quantitative field, or equivalent depth demonstrated through applied work.

Compensation

Annual base salary range: $160,000–$208,000, depending on relevant experience. This role is also eligible for equity and a variable annual bonus based on both company and individual performance.