Senior Engineering Manager, Machine Learning

About Checkr Checkr is building the data platform to power safe and fair decisions. Over 140,000 companies and millions of people rely on Checkr for AI verification in the moments that matter most: getting a new job, a new place to live, a car ride, childcare, even a date. Customers include Uber, Pennymac, Airbnb, Doordash, and Anthropic. We’re a team that thrives on solving complex problems with innovative solutions that advance our mission. Checkr is recognized on Forbes Cloud 100 2025 List and is a Y Combinator 2024 Breakthrough Company. We are hiring a Senior Engineering Manager to lead the Machine Learning Engineering team inside Checkr’s Data & ML organization. This team sits at the heart of our product's strategic advantage, making millions of background checks faster, more accurate, and more trustworthy. The team owns three connected areas: Core product intelligence: NLP and classification systems, entity resolution, profile integrity, model accuracy, and the production services that power Checkr’s products. Workforce integrity: resume and identity fraud, multi-signal risk detection, and new products that help customers identify sophisticated hiring fraud ML and agent infrastructure: evaluation systems, observability, model lifecycle, agent orchestration, and reusable infrastructure that helps every team ship reliable AI faster. This is an engineering leadership [... source excerpt omitted ...] le, not a research-management role: you will lead ML engineers across seniority levels, stay close enough to the work to set technical direction, and hold the team accountable for production outcomes. You will decide where ML should continue to create a strategic advantage for Checkr. You will also shape Checkr’s broader AI strategy. The near-term agenda includes building a measurable accuracy moat in our core products, launching an end-to-end workforce-integrity product, and creating the evaluation and knowledge infrastructure required to operate AI agents in a regulated domain. This role reports to the Sr. Director of Data & ML within Engineering. This role is based in San Franc [... source excerpt omitted ...] team that generates its own roadmap. Set the strategy and roadmap. Turn ambiguous company priorities into a focused, multi-quarter ML agenda. Put investment on work that improves customer outcomes, revenue, accuracy, reliability, or cost. Stop work that does not. Raise the production engineering bar. Models and agents ship as dependable software: clear APIs, tests, CI/CD, observability, on-call ownership, and defined reliability targets. Build the ML operating model. Establish shared approaches to evaluation, golden sets, training-data provenance, model and prompt versioning, monitoring, retraining, latency, and cost. Replace artisanal evaluation with repeatable systems. Adva