Applied AI Engineer at MaintainX

Job description

MaintainX is a leading mobile-first work execution platform for industrial and frontline teams. More than 13,000 customers, including Duracell, McDonald's, Shell, DHL and Volvo, use MaintainX to cut unplanned downtime and run better operations, across 13.9 million managed assets and 79.5 million completed work orders. In August 2026 MaintainX became part of Autodesk, joining Autodesk Operations Solutions adsknews.autodesk.com, the organization unifying Autodesk's operations platform alongside Tandem, FlexSim and Fusion Operations. Autodesk's strategy is to converge design, make and operate into one continuous lifecycle: design an asset, build it, run it, then feed what you learn running it back into the next design. Autodesk had design and make. Operate is the phase that tells you what actually happened, and it is ours. 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. 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 Our mission is to keep the physical world running. Factories, fleets, hospitals and campuses stay up because the people who maintain them have tools worth using. That is what we build. Compensation and benefits. Base pay is one part of the package. Depending on the role, compensation may also include commission, an annual bonus and equity. Benefits differ by country. For roles in the United States, Autodesk’s benefits are described at benefits.autodesk.com benefits.autodesk.com. For roles in Canada and other countries, the plan differs on health coverage, retirement and leave, and your recruiter will walk you through it. Belonging. We take pride in a culture where everyone can thrive. More at autodesk.com/company/global-belonging autodesk.com. More on where this is going: Autodesk CEO Andrew Anagnost on building the future of connected operations, and AOS SVP Stephen Hooper on welcoming MaintainX to Autodesk.

Requirements

Must-haves

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
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
Our mission is to keep the physical world running. Factories, fleets, hospitals and campuses stay up because the people who maintain them have tools worth using. That is what we build.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillm

Platform: ashby
Effort: quick