Applied AI Engineer

MaintainX (An Autodesk Company)

3d

About The Position

MaintainX is the world's leading AI-powered maintenance and asset management platform, serving 14,000+ customers including Duracell, Shell, Cintas, and Brenntag. We raised $150M in Series D funding led by Bessemer Venture Partners and Bain Capital Ventures, bringing our total funding to $254M. We were named to the Forbes 2025 Cloud 100, the definitive ranking of the top 100 private cloud companies in the world. We're growing fast and hiring the talent to match.
Frontier models are getting commoditized. The operating data underneath them isn't — and that's what we own. 13.9M+ managed assets, 79.5M+ completed work orders, and 150,000+ technicians generating trustworthy operating data every week, at the point of work.
Document Intelligence is the horizontal engine that turns that raw material — any file a customer hands us — into structured, trustworthy maintenance knowledge, and into the AI-native entities and answers built on top of it.
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."
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.

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 Haves

• 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

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

Benefits

• Competitive salary and meaningful equity opportunities.
• Healthcare, dental, and vision coverage.
• 401(k) / RRSP enrollment program.
• Take what you need PTO.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed