Machine Learning Engineer

Possible; Seattle (WA); Hybrid; USD 187,000 - 202,000; Full time

Benefits offered by this job

Hybrid work model

Downtown Seattle office

Stock options

Comprehensive benefits

Bonus plan

Job summary

Possible Finance is seeking the first dedicated owner of ML infrastructure in Seattle. You will design, deploy, and monitor a feature store, model serving, and drift detection, enabling data scientists to move faster while keeping models safe in production.

You’ll start as a team of one, build tooling from scratch, and shape the ML platform standards that power all models across the company. This hybrid role includes strong compensation, stock options, and benefits.

Qualifications

• Deep, hands-on experience building and operating machine learning infrastructure — feature stores, model serving, and monitoring systems — in a production environment.
• Track record of solving ambiguous, undefined problems; this role has no existing playbook and you’ll build one.
• Strong proficiency in Python, AWS, and Databricks; engage with broader MLOps tooling to choose the right tools.
• Demonstrated drive for results and accountability to a high bar for work.
• Comfort working cross-functionally with data scientists and engineers, bringing them along on new tooling.

Responsibilities

• Own ML infrastructure from scratch, design and rollout a shared feature store for experimentation.
• Establish drift monitoring to detect model degradation before customer impact.
• Consolidate deployment tooling into a reliable pipeline for models and learning iterations.

Skills

ML infrastructure

Feature stores

Model serving

Monitoring systems

Python

AWS

Databricks

Cross-functional collaboration

Self-starter

Tools

Databricks

AWS

Python

Job description

Team Introduction

Since our founding, we’ve delivered over $1 billion in funding to more than 1 million customers and saved them over $500 million. At Possible, we’re building a new kind of consumer finance company, one that helps people stay out of debt rather than profit from keeping them in it.

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 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.

Possible Finance

With the backing of our venture investors— Union Square Ventures, Canvas Ventures, Euclidean Capital, and Unlock Venture Partners — a dedicated following of hundreds of thousands of customers, and an extraordinary team, we are unwavering in our fight for financial fairness. As one of only a few FinTech Public Benefit Corporations, we’ve baked our dual dedication to building a profitable and socially impactful company into our charter; we only succeed when our customers do too. Give us a shout if you’d like to help us ship financial products that protect consumers from predatory lending practices and promote economic health.

Possible Finance is dedicated to financial fairness and community empowerment. We welcome diverse perspectives and experiences to help us achieve our mission of unlocking economic mobility for generations to come.

Learn more about us as a Public Benefit Company.