Machine Learning Engineer (AI)

Machine Learning Engineer

Company

Checkr

Conditions

Full-time Middle 🇺🇸 USA 💻 Development ✈️ Relocation Job description

What you’ll do

Build and deploy ML/AI services. Design, develop, and ship ML models and AI systems that Product Engineering teams rely on. You write the model code, the API layer, the monitoring, and the tests. Not notebooks; production services. Design with LLMs and APIs. Use LLM APIs (OpenAI, Anthropic, etc.) as building blocks in production systems. You know when to call an LLM, when to fine-tune, when to use a classical model, and when to write a rule. You think about cost, latency, and quality together. Ship production software. Write clean, well-structured code with solid OOP, proper abstractions, error handling, and tests. Your code gets reviewed by SWEs and passes. CI/CD is how you work, not something you bolt on at the end. Partner with product and engineering. Translate business problems into ML solutions. Define API contracts with product engineers. Explain your approach clearly to non-ML partners and leave the room with alignment, not confusion. Evaluate and iterate fast. Build evaluation frameworks, run experiments, and make data-driven decisions about model and system performance. Ship and iterate; don’t wait for perfect. Ship AI-powered workflows. Put AI to work on your own processes: automate pipelines, build agentic workflows, and contribute reusable skills and context to Checkr’s agentic platform. The expectation is that our teams operate AI-first.

What you bring

• 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; you write clean, testable, well-structured code with solid OOP instincts. Not scripts; software
• Hands-on experience using LLM APIs in production systems: prompt engineering, structured outputs, function calling, cost management, and evaluation
• You’ve built and maintained APIs, worked with CI/CD pipelines, and shipped 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; experience using LLMs, code-generation tools, or agentic systems in production or operational contexts is a strong signal
• You can evaluate tradeoffs: fine-tune vs. prompt, hosted vs. self-deployed, classical ML vs. LLM, rule vs. model
• Strong communication skills; you explain technical decisions clearly to engineers and non-engineers alike, without hiding behind jargon
• You use AI tools (Copilot, Claude, etc.) to move faster, but you understand every line they produce. You can spot AI slop and you don’t ship it
• 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

• 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 (Checkr’s platform runs on Rails)
• Familiarity with compliance-sensitive domains (fintech, legal tech, HR tech)
• Working knowledge of dbt, Snowflake, or modern ELT/data transformation tools

What we offer

• A fast-paced and collaborative environment
• 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 and hub locations (Denver, CO; San Francisco, CA; Nashville, TN; Santiago, Chile) with in-office presence required 3+ days a week
• A relocation stipend may be available

Who you are

• Based in San Francisco or willing to relocate; strong collaboration with Product Engineering and cross-functional teams
• Customer-, impact-, and result-driven with a passion for building production-grade ML systems

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