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.