Senior Engineering Manager, Machine Learning
About this role
Checkr is building the data platform to power safe and fair decisions, using artificial intelligence verification to make background checks faster, more accurate, and more trustworthy for millions of people and over 140,000 companies. The Machine Learning Engineering team owns core product intelligence like NLP and classification systems, workforce integrity tools combating resume and identity fraud, and the evaluation and agent infrastructure that helps the broader organization ship reliable AI faster.
Leadership of this team involves guiding machine learning engineers across seniority levels, setting technical direction, and ensuring models and agents operate as dependable software with clear APIs, testing, and observability. The person in this role will shape Checkr's broader artificial intelligence strategy, focusing on building a measurable accuracy moat, launching an end-to-end workforce integrity product, and establishing robust evaluation systems for a regulated domain. Someone who prefers shipping over heavy process and values direct ownership will thrive in this environment.
What you will do
- Lead, hire, and develop machine learning engineers across multiple seniority levels while building a team that generates its own roadmap.
- Translate ambiguous company priorities into a focused, multi-quarter machine learning agenda that improves customer outcomes and reliability.
- Raise the production engineering bar by ensuring models and agents ship with clear APIs, automated tests, CI/CD, and on-call ownership.
- Establish shared approaches to evaluation, data provenance, model versioning, monitoring, latency, and cost management.
- Guide systems for classification, information extraction, entity resolution, and profile integrity to make model quality measurable in production.
- Partner with product, security, and legal teams to turn signals across identity, resume, and employment data into customer-facing fraud products.
- Guide the design of agentic AI systems that combine specialized models, large language models, tools, and governed knowledge.
What you need
- Ten or more years of experience building software and machine learning or artificial intelligence systems with clear career progression.
- Three or more years of management experience overseeing machine learning or software engineers, including hiring senior and staff-level talent.
- Strong software engineering foundation and a proven record of shipping machine learning systems that operate reliably in production.
- Technical depth spanning the entire machine learning lifecycle from data labeling and experimentation to deployment, monitoring, and incident response.
- Fluency with modern large language model systems, including structured outputs, tool use, agent orchestration, and evaluation trade-offs.
- Executive-level communication skills capable of making complex technical trade-offs legible to senior leadership to drive prompt decisions.
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.
Benefits
- Competitive cash and equity compensation with opportunity for advancement
- 100% medical, dental, and vision coverage
- Up to $25K reimbursement for fertility, adoption, and parental planning services
- Flexible PTO policy and monthly wellness stipend
- Learning and development allowance
Skills and tech stack
Questions candidates ask
What is the work location policy for this role?
This role is based in San Francisco, California, and individuals are expected to work from the office three or more days a week as part of the company hub location guidelines.
What is the salary range for this position?
The base salary range for this position in San Francisco, California is $302,000 to $355,000 USD.
What are the immediate focus areas for the team?
The near-term agenda includes building a measurable accuracy moat in core products, launching an end-to-end workforce integrity product, and creating evaluation and knowledge infrastructure to operate AI agents in a regulated domain.
Does this role involve hands-on research?
This is an engineering leadership role rather than a research-management role where leaders stay close enough to the work to set technical direction and hold the team accountable for production outcomes.