Senior Machine Learning Engineer

Responsibilities

Design and roll out a shared feature store for data-science teams. Build drift-monitoring systems to identify model degradation before it affects customers. Consolidate model experimentation and deployment tooling into a clean, reliable pipeline. Own Possible’s ML infrastructure strategy, standards, and processes as the first dedicated infrastructure engineer. Partner cross-functionally with data scientists and engineers to drive adoption of new tooling.

Requirements

Deep, hands-on production experience building and operating machine learning infrastructure, including feature stores, model serving, and monitoring systems. Experience solving ambiguous and undefined problems without an existing playbook. Strong proficiency in Python, AWS, and Databricks. Broad familiarity with MLOps tooling and the judgment to evaluate and select appropriate tools. Strong ownership, accountability, and results orientation. Ability to work collaboratively with data scientists and engineers and build consensus around new tooling. Self-starter comfortable being the first person in a role and building a function from scratch.

Benefits

Hybrid work arrangement with three days per week in the downtown Seattle office on Monday, Tuesday, and Thursday. Significant stock options, comprehensive benefits, a bonus plan, commuter benefits, and complimentary office drinks and food. Excellent office space and collaboration with a mission-driven Public Benefit Corporation focused on financial fairness.