Data Scientist, Machine Learning

Company: Middesk
Location: San Francisco
Posted on: April 2, 2026

Job Description:

About Middesk Middesk makes it easier for businesses to work together. Since 2018, we’ve been transforming business identity verification, replacing slow, manual processes with seamless access to complete, up-to-date data. Our platform helps companies across industries confidently verify business identities, onboard customers faster, and reduce risk at every stage of the customer lifecycle. Middesk came out of Y Combinator, is backed by Sequoia Capital and Accel Partners, and was recently named to Forbes Fintech 50 List. The Role We are actively building AI-driven applications that streamline customer workflows, focusing on business onboarding. With our proprietary identity data assets and deep domain expertise, we are uniquely positioned to expand into a broader set of AI-powered solutions that drive long-term growth. We’re looking for a hands-on applied ML expert to help build the technical foundation for these efforts. Ideally you have shipped external-facing models in the risk/fraud space and know the messy realities of imbalanced data, low labels, and changing behavior. This is a highly technical, hands-on role with wide influence on how we design, build, and scale ML at Middesk. What You'll Do: Build risk & fraud ML applications: Deliver production ML models in fraud, trust & safety, KYB, and compliance domains, with measurable impact on customer workflows. Tackle hard data problems: Work on classification problems with extreme class imbalance, sparse signals, and “cold start” label challenges. Innovate in feature engineering & labeling: Use graph-based techniques, weak supervision, LLMs, and AI agents to improve signal extraction and automate labeling process. Establish ML infrastructure foundations: Partner with platform engineering team to design feature services, model training pipeline, model serving standards, and orchestration to scale multiple ML use cases. What We’re Looking For: 7 years applied ML experience, with direct impact in risk, fraud, trust & safety, compliance, or adjacent high-stakes domains. Proven track record of shipping ML models from research to production in external-facing products. Expertise in classification with real-world challenges, for example: imbalanced labels, sparse signals, cold start, and production version management. Hands-on ML infrastructure experience: feature stores, model management, ML training/serving pipelines. Comfort as a senior IC: setting technical direction, mentoring peers, and establishing best practices. Nice to Haves: B2B SaaS experience, ideally building ML products for enterprise customers. MLE/engineering collaboration experience, or direct MLE work on ML pipelines and services. Familiarity with graph, LLM-based feature generation, or AI agent workflows. Experience scaling ML across multiple products or risk domains.

Keywords: Middesk, Davis , Data Scientist, Machine Learning, Engineering , San Francisco, California