Senior Machine Learning Engineer
Possible Finance11 hours ago
Base Salary
$187k - $202k/yr
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.
Categories
About Possible Finance
Possible Finance builds a mobile-first consumer lending platform offering small-dollar installment loans and credit-building products for underserved U.S. borrowers. The company earns revenue from lending fees and interest, and operates as a Public Benefit Corporation focused on affordable credit. Founded in 2017 and headquartered in Seattle, it has a distributed team and more than 100,000 App Store reviews with a 4.8-star average.