MiddeskData Scientist, Machine Learning
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.