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Clerk is a developer infrastructure company providing user authentication and identity management for modern web applications. The company offers full-stack React components and APIs that enable developers to build identity, organization management, and billing flows.
You will join a deliberately small, very senior data team to own machine learning and the product-facing side of Clerk's data infrastructure. This is a greenfield role where you will take ambiguous problems from initial question through production deployment.
Key responsibilities:
- Build Clerk's ML infrastructure: establish pipelines, training systems, and model serving that move ML from notebook to production, leveraging engineering's existing infrastructure rather than creating parallel systems.
- Turn internal data into products: develop internal signals (conversion and expansion scoring, lifetime value, feature telemetry), then ship useful signals to customers. Create a semantic layer providing natural-language access to customer data.
- Ship in engineering repositories: land models and signals directly in product and platform code alongside the teams that own it. Drive cross-team work independently.
- Contribute broadly to Clerk's success: build dashboards, investigate trends, engage with customers. No task is beneath you; you prioritize company success over job description boundaries.
You are a Staff-level Data Scientist or ML Engineer who has built machine learning systems from scratch and operated them in production. You are equally comfortable building and analyzing. You own large, ambiguous problems as an individual contributor seeking significant scope and autonomy. You bridge data and engineering, comfortable in the data stack and equally at home opening pull requests in engineering repositories. You have strong product sense and know which problems merit solving. You work iteratively with a "ship and iterate" mentality suited to fast-growing startups. You have technical fluency in rigorous data, ML, and infrastructure work with high standards and strong opinions.
Requirements:
- Staff-level Data Scientist or ML Engineer with proven experience building machine learning systems from scratch and running them in production
- Located in the Americas within ±3 hours of U.S. Mountain Time (MDT, UTC−06:00)
- Individual contributor mindset with desire for significant scope and autonomy
- Ability to cross between data and engineering; comfortable opening pull requests in engineering repositories and learning adjacent team workflows
- Iterative, "ship and iterate" work style suited to startup pace
- Technical fluency in rigorous data, ML, and infrastructure practices
- Familiarity or quick learning ability with tech stack: GCP/BigQuery, dbt, Dagster, ClickHouse, PostHog, Hex
- Experience in developer tools or infrastructure products is a plus
About Clerk
SaaS / Enterprise Software — user authentication and identity management for modern web apps.
