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Salary: USD 207,000 - 244,000 / annual
Checkr is building a data platform to power safe and fair decisions. The company processes millions of background checks annually and is recognized on the Forbes Cloud 100 2025 list and as a Y Combinator 2024 Breakthrough Company. Major customers include Uber, Airbnb, DoorDash, and Anthropic.
You will join the ML team within Checkr's Data & ML organization to build and ship AI systems that power the company's core products. The ML team develops production services for document processing, charge classification, entity resolution, and in-product intelligence that Product Engineering teams depend on daily.
This is a production-focused role, not research or notebook work. You will own ML services end-to-end: designing, coding, deploying, and monitoring them. The role emphasizes building AI-native software with strong engineering craft, using LLMs and APIs as first-class tools, and shipping production-quality code rather than experimental prototypes.
Key responsibilities include:
- Design, develop, and ship ML models and AI systems that other engineering teams rely on, including model code, API layers, monitoring, and tests
- Use LLM APIs (OpenAI, Anthropic, etc.) as building blocks in production systems, making informed decisions about when to call an LLM, fine-tune, use classical models, or write rules
- Write clean, well-structured production code with solid object-oriented design, proper abstractions, error handling, and comprehensive tests
- Partner with Product and Engineering teams to translate business problems into ML solutions and define API contracts
- Build evaluation frameworks, run experiments, and make data-driven decisions about model and system performance
- Contribute to Checkr's agentic platform by automating pipelines, building agentic workflows, and creating reusable skills and context
You will work in a fast-paced, impact-first environment with less process and more shipping. The role sits in the central Data & ML team and requires daily partnership with Product Engineering, Product, and cross-functional teams. You will also contribute to Checkr's broader AI strategy, including deployment of agentic fleets and building scalable context with semantic layers.
Checkr operates a hybrid model with hub locations in Denver, San Francisco, Nashville, and Santiago. Individuals are expected to work from the office 3+ days per week. In-office perks include lunch five times weekly, commuter stipend, and snacks/beverages. A relocation stipend may be available for those willing to relocate to a hub location.
REQUIREMENTS:
- Bachelor's or Master's degree in Computer Science, Mathematics, or related technical field, or equivalent depth from experience
- 6+ years building software professionally, with at least 2 years building ML systems that run in production
- Strong Python fluency with clean, testable, well-structured code and solid OOP instincts
- Hands-on experience using LLM APIs in production systems: prompt engineering, structured outputs, function calling, cost management, and evaluation
- Experience building and maintaining APIs, working with CI/CD pipelines, and shipping code that other engineers depend on
- Comfort with and enthusiasm for AI-assisted workflows; experience using LLMs, code-generation tools, or agentic systems in production or operational contexts
- Ability to use AI tools (Copilot, Claude, etc.) to move faster while understanding every line produced and spotting/avoiding AI slop
- A-player mindset with strong bias for action: raises the bar, moves with urgency, stays resilient through ambiguity, takes ownership to deliver meaningful outcomes
NICE TO HAVE:
- Experience with MLOps platforms (MLflow, SageMaker, Vertex, or similar)
- Background in document processing, OCR, or information extraction
- Experience with PySpark or large-scale data processing
- Ruby experience (Checkr's platform runs on Rails)
- Familiarity with compliance-sensitive domains (fintech, legal tech, HR tech)
- Working knowledge of dbt, Snowflake, or modern ELT/data transformation tools
