Checkr
Senior Engineering Manager, Machine Learning
San Francisco, California, United States · Senior
Sponsorship not specified$302k-$355kDetected 44 days ago
PythonSnowflakeAWSCI/CDMachine LearningDeep LearningSparkNLPLLMsAgentic AIMLOpsAI OrchestrationA/B TestingResearchLeadershipCommunicationCollaboration
Stay score
odds of building a lasting career here
59Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →H-1B wage level
the lottery is wage-weighted — each level is one more entry
Level I$167,5651 entry
Level II$208,1042 entries
Level III$248,6433 entries
Level IV$289,1824 entries
This range already reaches Level IV — the maximum four lottery entries.
DOL prevailing wage, 2026-27 wage year · Computer and Information Systems Managers (11-3021) · San Francisco-Oakland-Fremont, CA. Wage level is derived by USCIS from the offered wage, occupation and worksite; the occupation shown is inferred from the job title.
Employer immigration record
from this employer's Department of Labor filings
Files H-1B transfers
4 transfer filings in the last year, covering 4 workers. Median labor-condition decision: 8 days. An employer that already files transfers is one that can take over an existing H-1B.
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
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About the role
- 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.
- This team sits at the heart of our product's strategic advantage, making millions of background checks faster, more accurate, and more trustworthy.
Responsibilities
- 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.
- 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.
- 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.
Requirements
- Experience setting direction for NLP, classification, extraction, entity resolution, risk, fraud, recommendation, or similarly complex applied-ML domains.
- 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.
- 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.
Nice to have
- Built ML infrastructure or MLOps platforms used by multiple teams.
- 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.
- Pay Transparency Disclosure
- For more information on our compensation philosophy, see our website.
- A fast-paced and collaborative environment
Skills
- About Checkr
- 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.
- Checkr is recognized on Forbes Cloud 100 2025 List and is a Y Combinator 2024 Breakthrough Company.
Compensation
- For more information on our compensation philosophy, see our website.
- OR Base Salary range (San Francisco, CA)
- $302,000 - $355,000 USD
Benefits
- Learning and development allowance
- Competitive cash and equity compensation, and opportunity for advancement
- 100% medical, dental, and vision coverage
- Up to $25K reimbursement for fertility, adoption, and parental planning services
- Flexible PTO policy
- Monthly wellness stipend
- 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.
Company info
- 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 listing is sourced directly from Checkr's careers page and normalized into a canonical job model.