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MaintainX is a leading mobile-first work execution platform for industrial and frontline teams. More than 15,000 customers , including McDonalds, Michaels, Cintas, Redimix, and U.S. LBM, use MaintainX to cut unplanned downtime and run better operations, across 16 million managed assets and 92 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.

As we enter our next phase of growth, we’re investing deeply in AI/ML, LLMs, and Industrial IoT to transform how frontline teams operate—predicting failures before they happen, automating workflows, and embedding intelligence into every asset and procedure.

The Role

We are seeking a highly skilled and motivated Senior Applied Machine Learning Developer to guide the technical direction and architecture of our Predictive Maintenance and Asset Intelligence initiatives.

You’ll combine deep ML expertise with strong software development and leadership skills—mentoring developers, scaling systems, and driving the roadmap for AI-enabled maintenance intelligence across thousands of industrial sites.

This role sits at the intersection of ML architecture, IoT data systems, and product impact, shaping the foundation for MaintainX’s predictive and generative AI strategy.

What you’ll do

• Lead technical direction for predictive maintenance, anomaly detection, and LLM-powered intelligence across MaintainX products.
• Architect end-to-end ML systems—from data ingestion and feature development to model training, deployment, and monitoring.
• Mentor a growing team of ML and data developers, instilling best practices for experimentation, evaluation, and model lifecycle management.
• Partner with product and software development leaders to align AI roadmap with customer needs and business goals.
• Design reliable data and feedback loops that connect customer telemetry and operator feedback to model retraining.
• Drive performance optimization through techniques like quantization, distillation, and scalable inference serving.
• Work with LLM frameworks (LangChain, LlamaIndex, Hugging Face) to build reasoning systems and agentic workflows for asset and work intelligence.
• Ensure ML infrastructure meets production standards for latency, reliability, explainability, and security.