Checkr
Data platform
MachineLearningEngineer
“Machine Learning Engineer at Checkr. Skills: Machine Learning, LLM APIs, Production Software, Python. Build and ship AI systems. Design with LLMs and APIs”
What You'll Achieve.
deliver meaningful outcomes
Industry & Context.
solving complex problems; Translate business problems into ML solutions; evaluate tradeoffs
Individuals are expected to work from the office 3+ days a week, relocation stipend may be available
What They're Looking For.
Must Have
4+ years building software professionally, at least 2 of those building ML systems that run in production, Python you 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, evaluation, built and maintained APIs, worked with CI/CD pipelines, 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 experience using LLMs, code-generation tools, or agentic systems in production or operational contexts, evaluate tradeoffs, fine-tune vs. prompt, hosted vs. self-deployed, classical ML vs. LLM, rule vs. model, explain technical decisions clearly to engineers and non-engineers alike, use AI tools (Copilot, Claude, etc. ) to move faster, understand every line they produce, spot AI slop, An A-player mindset with a bias for action, raise the bar, move with urgency, stay resilient through ambiguity, take ownership to deliver meaningful outcomes
Nice to Have
Experience with MLOps platforms (MLflow, SageMaker, Vertex, or similar), Background in document processing, OCR, 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
What You'll Do.
Build and ship AI systems
Design with LLMs and APIs
Use LLM APIs as building blocks
Ship production software
Partner with product and engineering
Translate business problems into ML solutions
Explain approach clearly
Evaluate and iterate fast
Build evaluation frameworks
Make data-driven decisions
Ship AI-powered workflows
Build agentic workflows
Contribute reusable skills and context
How You'll Work.
Team & Collaboration
Partner with product and engineering; Define API contracts with product engineers; Explain your approach clearly to non-ML partners; leave the room with alignment; explain technical decisions clearly to engineers and non-engineers alike
Communication Scope
explain technical decisions clearly to engineers and non-engineers alike; without hiding behind jargon
Applying for this Machine Learning Engineer role?
Most applicants get filtered before a human reads their resume. See if yours makes the cut.
How to Apply on Greenhouse
- Create a Greenhouse profile before applying — it saves time across multiple applications.
- Upload your resume as a PDF; the parser handles it better than Word.
- Answer all knockout questions carefully — wrong answers auto-reject before a human sees you.
- Enable email notifications to track application status in real time.
ANONYMOUS · UNFILTERED
What do employees actually say about Checkr?
Real rants from real employees. Read before you apply.