Data Scientist

Clerk.dev

. Build Clerk’s machine learning infrastructure, including pipelines, training, and model serving from notebook to production .

Posted 10/22/2026full-timeRemote • California • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying machine learning systems, with a strong focus on data product development and integration within engineering environments. Proficient in utilizing cloud platforms and data tools to drive insights and enhance product offerings.

Highest-signal resume keywords

Machine Learning Infrastructure DevelopmentData Product DevelopmentGCP/BQ FamiliarityIterative Shipping ApproachCross-Team Collaboration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningData AnalysisModel ServingPipeline DevelopmentFeature TelemetryConversion ScoringLifetime Value AnalysisProduct SenseInfrastructure DevelopmentData Integration

Soft Skills

AutonomyCommunicationProblem-Solving

Tools & Technologies

GCPBigQueryDbtDagsterClickHousePostHogHex

Industry Keywords

Data ScienceMachine Learning EngineeringDeveloper ToolsInfrastructure Products

Tech Stack

Tools & technologies

Google Cloud Platform

About the role

Key responsibilities & impact

Build Clerk’s machine learning infrastructure, including pipelines, training, and model serving from notebook to production
Turn Clerk’s internal data into products, including conversion and expansion scoring, lifetime value, and feature telemetry
Ship useful data products to customers, including a semantic layer for natural-language access to their data
Integrate models and signals into product and platform code in engineering repositories
Drive cross-team work with the teams owning product and platform code
Build dashboards, investigate trends, and speak with customers as needed

Requirements

What you’ll need

Staff-level Data Scientist or ML Engineer
Built machine learning systems from scratch and run them in production
Comfortable building as well as analyzing
Experience in developer tools or infrastructure products is a plus
Must live in the Americas
Must live within ±3 hours of U.S. Mountain Time (MDT, UTC−06:00)
Individual contributor seeking significant scope and autonomy
Comfortable working across data and engineering, including opening pull requests in adjacent engineering repositories
Product sense to identify worthwhile problems
Iterative “ship and iterate” approach
Technical fluency in data, machine learning, and infrastructure
Familiarity with or ability to quickly learn GCP/BQ, dbt, Dagster, ClickHouse, PostHog, and Hex

Benefits

Comp & perks

Equity ownership / stock option plan
Top-tier health insurance
Home office gear of choice
Unlimited vacation policy (25 days per year recommended)
National holidays specific to country of residence
Diverse and inclusive globally distributed team