Senior Machine Learning Engineer
Role summary
Checkr is seeking a Senior Machine Learning Engineer (P3) to join their Data & ML team within Engineering. This role focuses on building and deploying production-grade AI and ML services that enhance core product functionalities like document processing and entity resolution. The engineer will own services end-to-end, including design, coding, deployment, and monitoring, with a strong emphasis on writing production software, utilizing LLM APIs, and ensuring code quality. The position requires 6+ years of professional software development experience, with at least 2 years in production ML systems, and strong Python fluency. Experience with LLM APIs in production, CI/CD, and AI-assisted workflows is crucial. The role is based in San Francisco and operates in a hybrid work model.
About CheckrCheckr 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.
About the team/role
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.
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.
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.
This role sits in the central Data & ML team within the Engineering organization. You will partner daily with Product Engineering, Product, and cross-functional teams. You’ll also contribute to Checkr’s broader AI strategy, including our initiative to deploy our agentic fleet and build scalable context with our semantic layer.
We are looking for someone based in San Francisco who has built ML systems in fast-moving, impact-first environments. Less process, more shipping. Less paperwork, more results.
What you’ll do
What you bring
Nice to have
Pay Transparency Disclosure
We use geographic cost of labor as an input to develop ranges for our roles and as such, each location where we hire may have a different range. If this role is remote, we have listed the top to the bottom of the possible range, but we will specify the target range for an exact location when you are selected for a recruiting discussion. For more information on our compensation philosophy, see our website.
What We Offer
At Checkr, we believe an in office work environment strengthens collaboration, drives innovation, and encourages connection. Our hub locations are Denver, CO; San Francisco, CA; Nashville, TN; and Santiago, Chile. Individuals are expected to work from the office 3+ days a week. In-office perks are provided, such as lunch five times a week, a commuter stipend, and an abundance of snacks and beverages. A relocation stipend may be available for those willing to relocate to a Checkr hub location.
Equal Employment Opportunities at CheckrCheckr is committed to building the best product and company, which requires hiring talented and qualified individuals with a diverse set of perspectives and lived experiences. Checkr believes in hiring people of all backgrounds, including those whose histories are impacted by the justice system in accordance with local, state, and/or federal laws, including the San Francisco’s Fair Chance Ordinance.
Applicant Privacy PolicyIf you are a California resident or are located in Alberta or British Columbia, our Applicant Privacy Policy applies to our collection and processing of your personal information when you apply for a role with us or otherwise participate in our recruitment process.
*Legitimate Checkr emails will always include our official domain name after the @ symbol (e.g., name@checkr.com or name@ext.checkr.com).
Sample Checkr interview questions
- 1
Continuous Subarrays Sum Equals K Find the total number of continuous subarrays whose sum equals K. Input: nums = [1,2,3], k = 3 Output: 2 Explanation: Both the contiguous subarray [1,2] and the single-element subarray [3] sum perfectly to the target of 3.
codingmedium - 2
Dot Product of Two Sparse Vectors Calculate the dot product of two sparse vectors. Input: nums1 = [0,1,0,0,2], nums2 = [0,0,0,0,5] Output: 10 Explanation: Multiplies the aligned non-zero values (2 * 5) and ignores all the zeros, yielding a dot product of 10.
codingmedium - 3
Alien Dictionary Order Derive the alien dictionary order from a sorted list of alien words. Input: words = ["z","x","z"] Output: "" (Empty String) Explanation: The letter 'z' cannot come before 'x' and then suddenly after 'x', creating a cycle and making the dictionary invalid.
codingmedium - 4
Palindrome After Deleting One Character Determine if a string can be a palindrome after deleting at most one character. Input: s = "abc" Output: FALSE Explanation: Deleting any single character leaves either "ab", "bc", or "ac", none of which result in a valid palindrome.
codingmedium - 5
Split Array into Consecutive Subsequences Split an array into consecutive subsequences. Input: nums = [1,2,3,4,4,5] Output: FALSE Explanation: The numbers can form [1,2,3,4], but the remaining leftover group [4,5] is too short to form a valid sequence of length 3.
codingmedium
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