Senior Machine Learning Engineer

Responsibilities

• Design and roll out a shared feature store for data science experimentation and production use.
• Build drift monitoring to detect model degradation before it affects customers.
• Consolidate model experimentation and deployment tooling into a clean, reliable pipeline.
• Own Possible’s ML infrastructure strategy, processes, and standards as the first dedicated infrastructure specialist.
• Work cross-functionally with data scientists and engineers to adopt new tooling.

Requirements

• Deep, hands-on production experience building and operating machine learning infrastructure, including feature stores, model serving, and monitoring systems.
• Track record of solving ambiguous and undefined problems without an existing playbook.
• Strong proficiency in Python, AWS, and Databricks.
• Engagement with the broader MLOps tooling landscape and sound judgment in evaluating and selecting tools.
• Demonstrated accountability and drive for concrete results.
• Comfort collaborating cross-functionally and bringing colleagues along on new tooling.
• Self-starter mindset and enthusiasm for building systems from scratch.

Benefits

• Hybrid position requiring work from the downtown Seattle office three days per week on Monday, Tuesday, and Thursday.
• Significant stock options, comprehensive benefits, a bonus plan, commuter benefits, and complimentary drinks and food in the office.