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 role, 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 Francisco. We are looking for someone who is seeking less process and more shipping, less paperwork and more results.

What you’ll do

• Lead and grow the ML Engineering team. Hire, coach, and develop ML engineers. Set clear ownership, grow technical leaders, and build a 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.
• Advance core product intelligence. Guide systems for classification, information extraction, entity resolution, profile integrity, and accuracy. Make model quality measurable in production and drive the feedback loops that improve it.
• Launch new AI and fraud products. Partner with Product, Security, Operations, and go-to-market teams to turn signals across identity, resume, device, and employment data into customer products.
• Lead Checkr’s agentic transition. Guide the design of AI systems that combine specialized models, LLMs, tools, and governed knowledge. 
• Operate as an executive partner. Explain technical choices and risks in plain language. Align Product Engineering, Product, Operations, Legal, Security, and company leadership when incentives or constraints conflict.
• Model AI-native leadership. Use AI to increase the team’s speed and ambition. Keep ownership of every output. As generated code becomes cheaper, raise the bar on judgment, verification, and system design.

What you bring

• 10+ years building software and machine learning or AI systems, with a clear progression in scope and impact.
• 3+ years managing ML or software engineers, including hiring and developing senior and staff-level technical leaders.
• A strong software-engineering foundation and a record of shipping ML systems that run in production.
• Technical depth across the ML lifecycle: data and labeling, experimentation, model selection, deployment, APIs, CI/CD, observability, evaluation, retraining, and incident response.
• Sound judgment across classical ML, deep learning, LLMs, rules, and conventional software.
• Experience setting direction for NLP, classification, extraction, entity resolution, risk, fraud, recommendation, or similarly complex applied-ML domains.
• Fluency with modern LLM systems, including structured outputs, tool use, agent orchestration, retrieval, evaluation, latency, quality, and cost trade-offs.
• A record of translating an ambiguous business problem into a sharp roadmap, measurable outcomes, and shipped systems.
• Strong people leadership. You create leverage through clear ownership, candid feedback, delegation, and coaching.
• Executive-level communication and cross-functional influence. You can make a hard technical trade-off legible to senior leaders and leave the room with a decision.
• You use modern AI tools to move faster, verify their output, and can spot plausible-looking work that should not ship.
• An A-player mindset with a strong bias for action: you raise the bar, move with urgency, stay resilient through ambiguity, and take ownership to deliver meaningful outcomes.

Nice to have

• Built ML infrastructure or MLOps platforms used by multiple teams.
• Document intelligence, OCR, graph systems, identity resolution, trust and safety, fraud, or risk.
• Operated ML in a compliance-sensitive domain such as fintech, legal tech, HR tech, healthcare, or security.
• Python, AWS/SageMaker or similar cloud ML platforms, Snowflake, Spark, and modern data tooling.
• Launched a new AI product from early customer discovery through production scale.

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