Get money in minutes, build credit history and avoid hidden fees with Possible Finance. Apply in the app with just a few taps and manage money on your terms. Backed by USV.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this role
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.
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...
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