Quick overview
Job description
What you’ll do
- Build and ship AI features for document understanding, structured extraction, and review workflows.
- Develop reliable pipelines that combine language models with application logic, validation, and human review.
- Design evaluations using representative examples, labeled data, and clear quality measures.
- Improve output quality through prompting, retrieval, model selection, and structured generation.
- Build safeguards for uncertain, incomplete, or conflicting inputs.
- Partner with product, design, and domain experts to turn workflow needs into useful AI capabilities.
- Work with engineers to integrate AI services into APIs, background jobs, and customer-facing applications.
- Monitor quality, latency, and cost in production, and use the results to guide improvements.
- Document system behavior, limitations, and operational considerations.
What you’ll bring
- 5–8 years of engineering experience, including hands-on work building and shipping applied AI or machine learning products.
- Experience using language models for practical product workflows.
- Strong Python skills and experience building production services or data pipelines.
- Experience evaluating model outputs with measurable criteria, not just anecdotal review.
- Familiarity with structured outputs, tool use, retrieval, and validation techniques.
- Good judgment about model uncertainty and when a workflow should require human review.
- Ability to work with ambiguous problems and explain technical trade-offs clearly.
- Strong software engineering fundamentals, including testing, observability, and maintainable design.
Helpful experience
- Document AI, information extraction, OCR, or converting unstructured business documents into structured data.
- LLM evaluation, dataset curation, human-in-the-loop systems, and regression testing.
- FastAPI, PostgreSQL, cloud infrastructure, and background processing.
- Familiarity with privacy, data handling, and production monitoring for AI features.
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