Staff Machine Learning Engineer

Evolve is hiring a Staff Machine Learning Engineer to build and operate production machine-learning systems and establish the infrastructure and engineering standards that support its Data Product team. The role partners with data scientists and business teams to productionize models and LLM-based products, integrate them into workflows, and measure their business impact.

Responsibilities:

• 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

Requirements:

• 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
• 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. (No—duplicate omitted)
• 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