Machine Learning Engineer

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 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.

What you'll do

• 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.