MaintainX Logo

MaintainX

Applied AI Engineer

Posted 8 Days Ago
Hybrid
Seattle, WA, USA
152K-271K Annually
Entry level
Hybrid
Seattle, WA, USA
152K-271K Annually
Entry level
Own the generation layer of Document Intelligence by designing prompts, schemas, validators, multimodal context pipelines, and quality evaluations. Build production LLM features that transform video, audio, PDFs, images, OCR, transcripts, and keyframes into validated maintenance entities. Balance model quality, cost, and latency, monitor regressions, and collaborate with processing and domain engineering teams.
The summary above was generated by AI

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, 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. 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. 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.

Similar Jobs

17 Days Ago
Hybrid
Mid level
Mid level
Financial Services
Design, develop, and operate a production agentic data platform using Spark, Kafka, Airflow, Snowflake and AWS. Write secure high-quality code across frontend and backend, leverage enterprise AI-assisted development and LLMs (Claude Code, agents, RAG, embeddings), build autonomous agents and MCP server components, apply secure/responsible AI practices, troubleshoot systems, and own full-stack application/platform delivery with CI/CD, containers, and observability.
Top Skills: Agent Tool/Function CallingAirflowAngularAWSCi/CdClaude AgentsClaude CodeContainersEmbeddingsGraphQLJavaKafkaKotlinMcp ServerObservabilityPrompt ManagementPythonRagReactRestSnowflakeSparkTypescript
One Month Ago
Hybrid
Senior level
Senior level
Financial Services
Lead design and production of generative and agentic AI solutions, build ML pipelines and MLOps, define enterprise semantic modeling and ontology governance, implement RAG and responsible AI practices, and mentor engineers while partnering with stakeholders to deliver measurable business outcomes.
Top Skills: Agentic AiAPIsContinuous IntegrationData PipelinesGenerative AiLarge Language ModelsMlopsModel MonitoringOntologiesPythonReal-Time InferenceRetrieval-Augmented Generation (Rag)Semantic ModelingSemantic ReasoningUnit TestingWorkflow Orchestration
Yesterday
In-Office
140K-170K Annually
Entry level
140K-170K Annually
Entry level
Fintech
Build and improve production document-intelligence systems for commercial insurance using parsing, OCR, classification, structured extraction, reconciliation, retrieval, summarization, and deterministic logic. Evaluate model and parser behavior, investigate failures, maintain evaluation sets, compare technical approaches, collaborate with domain experts, and ship reliable Python and Azure Functions services with strong testing, observability, security, cost, and latency considerations.
Top Skills: Azure FunctionsDistributed ProcessingInformation ExtractionLarge Language ModelsOcrPythonRetrievalServerless ComputingStructured OutputsTool Calling

What you need to know about the Seattle Tech Scene

Home to tech titans like Microsoft and Amazon, Seattle punches far above its weight in innovation. But its surrounding mountains, sprinkled with world-famous hiking trails and climbing routes, make the city a destination for outdoorsy types as well. Established as a logging town before shifting to shipbuilding and logistics, the Emerald City is now known for its contributions to aerospace, software, biotech and cloud computing. And its status as a thriving tech ecosystem is attracting out-of-town companies looking to establish new tech and engineering hubs.

Key Facts About Seattle Tech

  • Number of Tech Workers: 287,000; 13% of overall workforce (2024 CompTIA survey)
  • Major Tech Employers: Amazon, Microsoft, Meta, Google
  • Key Industries: Artificial intelligence, cloud computing, software, biotechnology, game development
  • Funding Landscape: $3.1 billion in venture capital funding in 2024 (Pitchbook)
  • Notable Investors: Madrona, Fuse, Tola, Maveron
  • Research Centers and Universities: University of Washington, Seattle University, Seattle Pacific University, Allen Institute for Brain Science, Bill & Melinda Gates Foundation, Seattle Children’s Research Institute

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account