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Senior Machine Learning Engineer

Checkr

Denver, CO · $176k–$244k a year · Posted October 19, 2026

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Pay compared with the market

This listing pays about $210,000 a year. Across 115 open senior machine learning engineer listings that state pay, the middle half runs $197,500 to $255,000 and the median is $225,500. That is inside the middle half of those listings, 7% below the median.

Listed pay is what employers advertise, not what people are paid.

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About the job

<div class="content-intro"><p><span style="font-weight: 400;"><strong>About Checkr<br></strong></span>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.<br><br>We’re a team that thrives on solving complex problems with innovative solutions that advance our mission. Checkr is recognized on <a href="https://www.forbes.com/lists/cloud100/" target="_blank">Forbes Cloud 100 2025 List</a> and is a Y Combinator 2024 <a href="https://www.ycombinator.com/blog/yc-top-companies-2024">Breakthrough Company</a>.</p></div><p><strong>About the team/role</strong></p> <p>We’re hiring an ML Engineer (P3) to build and ship the AI systems that power Checkr’s core products. This role sits on the ML team inside Checkr’s Data & ML organization within Engineering.</p> <p>Checkr runs millions of background checks a year. The ML team builds the systems that make those checks faster, more accurate, and cheaper to operate: document processing, charge classification, entity resolution, and in-product intelligence. These are production services that Product Engineering depends on daily.</p> <p>This is not a research role or a notebook role. You’ll own ML services end-to-end: design them, code them, deploy them, monitor them. We need someone who writes production software, builds with LLMs and APIs as first-class tools, and can tell the difference between working code and AI slop. If you’ve spent the last few years building AI-native software and you care deeply about engineering craft, we want to talk.</p> <p>This