Staff Machine Learning Engineer
Evolve
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At Evolve, we’re on a mission to make vacation rental easy for everyone. Our high-performing, customer-obsessed team runs on curiosity, communication, and accountability—working together to create exceptional experiences for our owners and guests. Whether solving big challenges, delivering outstanding results, or celebrating wins, we approach every day with purpose and passion. If you’re ready to join a mission-driven company where every teammate has the opportunity to thrive, Evolve might just be the place for you. 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,…
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