Staff Machine Learning Engineer - Seattle

About Haus Haus is the causal marketing platform top businesses trust to optimize billions in ad spend worldwide. With support from PhD economists, data scientists, and growth experts, Haus’ AI-driven technology translates complex marketing measurement into clear action and outcomes, enabling brands like Dyson, Wayfair, Sonos, Fanduel, SharkNinja, and Intuit to optimize spend, accelerate growth, and make smarter marketing decisions at scale. THE ROLE This role will drive high-impact projects for advanced marketing planning, analysis, and optimization at Haus using optimization, machine learning, and causal inference. We are looking for individuals who not only excel in problem solving and critical thinking, but also are interested and proficient in writing production code, turning ideas to scalable systems. This role specifically will work deeply on the cMMM machine learning problem space. The role will be a blend of working with applied scientists, data scientists, data engineers and other MLEs to deliver trustworthy results to our customers while focusing on creating processes that help scale the business. WHAT YOU’LL DO - Drive initiatives from concept to final product delivery, ensuring seamless end-to-end execution: lead or contribute to the design, development, optimization, and product ionization of machine learning (ML) solutions for complex and high-impact problems. - Able to implement probabilistic techniques into reusable statistical libraries, including bootstrapping, statistical tests, and ML models/regressions. - Build and maintain the ML systems that power Haus’ product lines (specifically cMMM). - Review code and designs of teammates, providing constructive feedback. - Lead and collaborate with engineering and cross-functional partners across product, engineering, and science teams to drive system development from ideation to production. - Drive design and implementation of AI (Agentic) workflows for ML pipelines (including model validation) - Mentor