Team Introduction

Our Data Science and Data Engineering teams build the models that power how Possible extends credit responsibly - models that assess risk, detect fraud, and personalize outcomes for the people we serve. Today, though, the infrastructure behind those models - how features get built, how models get deployed, how we know when something's silently drifting - is maintained by the same people building the models themselves, layered on top of their core work. As Possible scales, that's becoming the bottleneck. We're looking for the person who takes ownership of that infrastructure long-term, so our data teams can focus on what they do best: building models that work.

The Role & Responsibilities

You'll be Possible's first dedicated owner of ML infrastructure - a green-field mandate with real autonomy to shape how we build, deploy, and monitor machine learning models going forward. In your first year, you'll design and roll out a shared feature store, giving our data teams a safe place to experiment with new features without ever touching production, and meaningfully improving how fast our models respond in real time. You'll bring visibility to a part of our systems that's currently a black box, standing up drift monitoring so we catch model degradation before it becomes a customer-facing problem. And you'll consolidate a patchwork of deployment tooling into one clean, reliable pipeline - covering not just the models we ship, but the ones we try and learn from along the way. This is a role for someone who takes ownership seriously: you'll start as a team of one, kickstarting the processes and standards Possible's ML function will run on for years, applying the same scientific rigor to your own infrastructure decisions that our data scientists apply to their models.

Requirements

• Deep, hands-on experience building and operating machine learning infrastructure - feature stores, model serving, and monitoring systems - in a production environment
• A track record of solving ambiguous, undefined problems: this role has no existing playbook at Possible, and you'll build one
• Strong proficiency in Python, AWS, and Databricks, with genuine engagement with the broader MLOps tooling landscape and the judgment to evaluate and choose the right tools for the job
• A demonstrated drive for results, holding yourself accountable to a high, concrete bar for your own work
• Comfort working cross-functionally with data scientists and engineers, bringing them along on new tooling rather than mandating it top-down
• A self-starter mindset - energized, not daunted, by being the first person in a role and building what it needs from scratch

What We Offer

This is a Hybrid position. We work in our centrally located downtown Seattle office three days a week (M, T, and Th).

The compensation range for this role is $187,440 to $202,350. We also offer significant stock options, comprehensive benefits, a bonus plan, commuter benefits, and excellent office space with complimentary drinks and food.