Applied AI Engineer

About the role

You'll own the Generation half of Document Intelligence: turning multimodal primitives (keyframes, transcripts, OCR) into schema-valid entities like SOPs, and holding the line on quality so "fast" never becomes "fast and wrong."

Responsibilities

• Design and iterate recipe prompts — system, few-shot, and context assembly — for each generation recipe
• Define per-entity target schemas and domain validators (procedure step-type rules, field caps) that generated output has to pass
• Build generation-quality eval datasets and rubrics, offline and online, running on LLMX's eval pipeline, and close the loop when quality regresses
• Assemble multimodal context windows from keyframes, transcript, and OCR so each model call has exactly what it needs
• Choose the model and token budget per recipe based on quality, cost, and latency tradeoffs

You'll ride on LLMX for model access and on Attachments for ingestion, and hand off validated entities to the domains that own them. You'll report to our Engineering Lead and work closely with the Processing side of Document Intelligence.

Minimum requirements

• Strong applied GenAI craft - prompt engineering, structured output / tool-use, RAG and retrieval-context patterns
• Real eval discipline: you've built datasets and rubrics, measured factuality/relevance/quality, and closed the loop on regressions yourself
• Shipped LLM features into production services, not notebooks, and can connect model performance to product impact
• Comfort working with multimodal inputs - video, PDF, audio, image - converted to text or structured output

Nice to have

• LLM observability / cost awareness, or experience with an eval platform
• Document, PDF, or video understanding; OCR; retrieval systems
• Light fine-tuning experience, or familiarity with the industrial/maintenance domain

Pay

$152K - $271K