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San Francisco, California, United States
Hybrid
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.
We’re building AI-driven applications that simplify customer workflows, starting with business onboarding. With our proprietary identity data and deep domain expertise, we’re in a strong position to expand into a broader set of intelligent, risk-aware products.
We’re looking for a hands-on engineer to help build the foundation for these systems. This role is less about inventing new ML algorithms and more about applying the right techniques to messy, real-world problems. You’ve worked in fraud, risk, or trust domains, and you understand how bad actors behave, how data breaks, and how to still ship reliable systems anyway.
This is a highly technical, hands-on role with broad influence over how we design, build, and scale data-driven systems at Middesk.
We follow a hybrid work model, and for this role, there is an expectation of 2 days per week in our SF/NYC office. Candidates should be based within a commutable distance, as we believe in the value of in-person collaboration and building strong team connections while also supporting flexibility where possible.
Build fraud & risk systems
Design and ship production systems that detect and prevent fraud across KYB, trust & safety, and compliance workflows.
Work with messy, real-world data
Tackle problems with extreme class imbalance, sparse signals, evolving adversarial behavior, and limited ground truth.
Leverage relationships in data
Apply graph-based approaches and entity resolution techniques to uncover hidden connections and improve risk detection.
Improve signal & labeling
Use a mix of heuristics, weak supervision, and modern AI tools (including LLMs where appropriate) to generate better features and labels.
Help scale our infrastructure
Partner with engineering to build and evolve systems for feature generation, model training, and production deployment across multiple use cases.
5+ years of experience in fraud, risk, or trust & safety
You’ve worked on real-world fraud or abuse problems and understand the domain deeply.
Experience building and shipping production systems
You’ve deployed models or data-driven systems that power external-facing products.
Strong foundation in applied ML or data systems
Comfortable working on classification problems with real-world constraints like imbalanced data, sparse signals, and changing patterns.
Experience with graph or relational data approaches
Familiarity with knowledge graphs, network analysis, or entity linking is strongly preferred.
Hands-on and pragmatic
You focus on impact over perfection and know how to balance speed, accuracy, and maintainability.