Machine Learning Engineer - AI Services | Checkr NEW

The Briefed; Denver (CO); On-site; USD 176,000 - 244,000; Full time

Benefits offered by this job

Catered lunches (in office)

Commuter stipend

Relocation allowances

Stocked office snacks

Learning & development allowance

Job summary

Checkr’s Data & ML team is hiring a Machine Learning Engineer (P3) to build production AI systems powering verification products. You will own end-to-end ML microservices, Python API layers, monitoring, and deployment via CI/CD.

You’ll work with LLMs (OpenAI, Anthropic) and traditional ML to deliver low-latency features. The role requires 6+ years in software engineering with at least 2+ years deploying ML/AI in production, plus strong Python and OOP skills.

Qualifications

• Bachelor’s or Master’s degree in CS, Mathematics, or related field.
• 6+ years of software engineering experience with 2+ years deploying ML/AI systems.
• Strong Python fluency and OOP.
• Hands-on experience with production LLM APIs (OpenAI/Anthropic).
• Experience building REST/gRPC APIs and CI/CD pipelines.
• Familiarity with AI coding tools (Copilot, Claude, Cursor).
• Bias for action, shipping, and measurable outcomes over theory.

Responsibilities

• Design, code, test, deploy, and monitor production ML microservices.
• Integrate LLM APIs with production pipelines and evaluate trade-offs.
• Write clean, modular code and manage deployments via CI/CD workflows.
• Build agentic workflows and reusable context skills on Checkr’s platform.
• Collaborate cross-functionally with Product Eng, PMs, and Data teams.

Skills

Python

OOP

LLM APIs

REST APIs

CI/CD

AI coding tools

Execution mindset

Team collaboration

Education

Bachelors or Masters in CS/Math/related field

Tools

MLflow

AWS SageMaker

Vertex AI

PySpark

Ruby on Rails

Job description

As a Machine Learning Engineer (P3) on the central Data & ML team inside Engineering, you will build and ship production AI systems powering Checkr’s core verification products—including document processing, charge classification, entity resolution, and in-product intelligence. This is not a research or notebook role. You will own end-to-end production microservices: designing models, writing Python API layers, setting up monitoring, establishing evaluation benchmarks, and deploying via automated CI/CD pipelines. You will leverage LLM APIs (OpenAI, Anthropic) alongside classical ML and rule-based systems to deliver high-quality, cost-effective, low-latency AI features, while contributing to Checkr’s broader agentic fleet and semantic context layer.

Key Responsibilities

Production ML/AI Services & LLM Architecture

• End-to-End Service Delivery: Design, code, test, deploy, and monitor production-grade ML microservices that Product Engineering depends on daily—writing clean Python APIs, model code, and comprehensive unit/integration tests.
• Pragmatic LLM Integration: Integrate commercial LLM APIs (OpenAI, Anthropic) into production pipelines using prompt engineering, structured outputs, function calling, fine-tuning, and cost/latency/quality trade-off evaluations.
• Production Software Craft: Write clean, object-oriented, modular code subject to rigorous software engineering code reviews, managing deployments through automated CI/CD workflows.
• AI-Powered Internal Workflows: Build agentic workflows, automated pipelines, and reusable context skills for Checkr's internal agentic platform to ensure engineering operates AI-first.

Data Science Evaluation & Product Alignment

• Evaluation & Benchmarking: Build evaluation frameworks, conduct offline/online experiments, and make data-driven decisions to continuously optimize model accuracy, precision, and operational cost.
• Cross-Functional Collaboration: Partner daily with Product Engineering, Product Managers, and Data teams to translate business problems into scalable ML solutions and clear API contracts.

Requirements

Technical Depth & Software Engineering Craft

• Educational Background: Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related quantitative technical field (or equivalent depth from practical experience).
• Professional Experience: 6+ years of professional software engineering experience, including at least 2+ years architecting and deploying ML/AI systems in production.
• Python & OOP Mastery: Strong Python fluency with solid Object-Oriented Programming (OOP) instincts, authoring clean, testable, and maintainable software.
• LLM APIs in Production: Hands-on experience integrating LLM APIs in production systems (structured outputs, function calling, prompt design, evaluation frameworks, and cost optimization).
• API & CI/CD Tooling: Proven experience building REST/gRPC APIs, operating CI/CD pipelines, and writing software relied on by external engineering teams.
• AI-Assisted Workflow Fluency: Comfort using modern AI coding tools (Copilot, Claude, Cursor) to move faster while retaining deep code comprehension to prevent shipping unvetted code.
• Execution Mindset: Strong bias for action, prioritizing rapid shipping, minimal overhead, and measurable business outcomes over theoretical research.

Preferred Qualifications

• Hands-on experience with production MLOps platforms (MLflow, AWS SageMaker, Vertex AI, or custom equivalents).
• Technical background in document processing, Optical Character Recognition (OCR), or information extraction pipelines.
• Experience processing large-scale datasets using PySpark or distributed compute clusters.
• Working knowledge of Ruby on Rails (Checkr’s underlying platform stack).
• Background operating within compliance-sensitive or regulated industries (HR tech, legal tech, fintech).
• Experience with modern ELT and data transformation tools (dbt, Snowflake).

Total Rewards & Benefits

• Compensation & Equity: Location-based base salary ($176,000 – $244,000 USD depending on region) + competitive equity options + career advancement opportunities.
• Healthcare Coverage: 100% employer-covered medical, dental, and vision insurance premiums for employees.
• Family & Parental Support: Up to $25,000 USD reimbursement for fertility, adoption, and parental planning services.
• Hub Perks & Commute: Daily catered lunches (5 days/week in office), commuter stipends, relocation allowances (for eligible hub relocations), and stocked office snacks/beverages.
• Time Off & Wellness: Flexible Paid Time Off (PTO), monthly wellness stipends, and dedicated learning and development allowances.