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Salary: USD 187,440 - 202,350 / annual
Possible is a consumer finance company that has delivered over $1 billion in funding to more than 1 million customers while saving them over $500 million. The company is building a new kind of finance platform focused on helping people stay out of debt rather than profiting from keeping them in it.
You will be Possible's first dedicated owner of ML infrastructure, with a green-field mandate and real autonomy to shape how the company builds, deploys, and monitors machine learning models. This is a high-impact individual contributor role where you'll take ownership of critical infrastructure that currently constrains the data science team.
In your first year, you will:
- Design and roll out a shared feature store, enabling data teams to safely experiment with new features without touching production and improving real-time model response times
- Bring visibility to currently opaque systems by standing up drift monitoring to catch model degradation before it affects customers
- Consolidate fragmented deployment tooling into a single, reliable pipeline covering both production models and experimental ones
You'll start as a team of one, establishing the processes and standards that will guide Possible's ML function for years to come. This requires applying scientific rigor to infrastructure decisions while working cross-functionally with data scientists and engineers to bring them along on new tooling rather than mandating changes top-down.
The role is hybrid, requiring three days per week in the downtown Seattle office (Monday, Tuesday, Thursday).
REQUIREMENTS:
- Deep, hands-on experience building and operating machine learning infrastructure in production environments, including feature stores, model serving, and monitoring systems
- Track record of solving ambiguous, undefined problems with no existing playbook
- Strong proficiency in Python, AWS, and Databricks, with genuine engagement with the broader MLOps tooling landscape and judgment to evaluate and choose the right tools
- Demonstrated drive for results with accountability to a high, concrete bar for your own work
- Comfort working cross-functionally with data scientists and engineers
- Self-starter mindset, energized by being the first person in a role and building it from scratch
