Machine Learning Engineer

Checkr•San Francisco, CA

7d•$168,000 - $198,000•Hybrid

About The Position

Checkr is seeking an ML Engineer (P2) to join their Data & ML organization within Engineering. This role focuses on building and deploying AI systems that power Checkr's core products, including document processing, charge classification, entity resolution, and in-product intelligence. The position is production-oriented, requiring end-to-end ownership of ML services from design and coding to deployment and monitoring. The ideal candidate will have experience building AI-native software and a strong understanding of engineering craft. This role involves partnering with Product Engineering, Product, and cross-functional teams, and contributing to Checkr's broader AI strategy, including agentic fleet deployment and semantic layer development. The company is looking for someone based in San Francisco with experience in fast-moving, impact-first environments.

Requirements

A Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field, or equivalent depth from experience.
4+ years building software professionally, with at least 2 of those building ML systems that run in production.
Strong Python fluency; ability to write clean, testable, well-structured code with 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.
Experience working with CI/CD pipelines.
Experience shipping code that other engineers depend on.
Comfortable with distributed systems concepts: queues, async processing, caching, horizontal scaling.
Experience with NLP tasks in production: classification, extraction, entity resolution, summarization.
Comfort with and enthusiasm for AI-assisted workflows.
Ability to evaluate tradeoffs: fine-tune vs. prompt, hosted vs. self-deployed, classical ML vs. LLM, rule vs. model.
Strong communication skills; ability to explain technical decisions clearly to engineers and non-engineers alike.
Ability to use AI tools (Copilot, Claude, etc.) effectively while understanding all produced code.
An A-player mindset with a strong bias for action: raising the bar, moving with urgency, staying resilient through ambiguity, and taking ownership to deliver meaningful outcomes.

Nice To Haves

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.
Familiarity with compliance-sensitive domains (fintech, legal tech, HR tech).
Working knowledge of dbt, Snowflake, or modern ELT/data transformation tools.

Responsibilities

Build and deploy ML/AI services, including model code, API layer, monitoring, and tests.
Design and implement production systems using LLM APIs (OpenAI, Anthropic, etc.) as building blocks, considering cost, latency, and quality.
Write clean, well-structured, testable code with solid OOP principles and utilize CI/CD pipelines.
Translate business problems into ML solutions and define API contracts with product engineers.
Explain technical approaches clearly to non-ML partners.
Build evaluation frameworks, run experiments, and make data-driven decisions for model and system performance.
Ship and iterate on ML solutions rapidly.
Automate personal and team processes using AI tools, build agentic workflows, and contribute to Checkr's agentic platform.
Operate AI-first within the team.

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
Lunch five times a week
Commuter stipend
Relocation stipend may be available

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