Senior Applied Scientist, Parts Intelligence & Inventory Optimization

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.

We're looking for a Senior Applied Scientist to own the intelligence layer behind our Parts Agent — one of the most strategic bets on our Inventory & EAM roadmap. The agent sits on top of a multi-layer parts data model (PartMaster, StockRecord, PhysicalInstance) and is responsible for answering hard inventory questions: when to reorder, how to optimize stock levels across sites, which parts are at risk of stockout, and how to reconcile messy supplier catalogs into a clean parts master. Your focus will be building the decision models, optimization routines, and AI-powered tools that make those answers trustworthy enough for enterprise maintenance teams to act on. This is a high-ownership role. You'll shape the modeling approach, partner closely with product and design on what inventory managers actually need, and ship iteratively against feedback from real enterprise customers. What you'll do

Own and evolve the optimization and ML models that power Parts Agent capabilities: reorder point prediction, economic order quantity, multi-site stock balancing, and demand forecasting.

Design and implement increasingly sophisticated inventory intelligence: vendor lead time modeling, criticality-weighted safety stock, substitution graph traversal, and proactive stockout alerting.

Build and maintain APIs and tools that expose these models to GenAI agent workflows (tool calling, structured input/output), enabling the Parts Agent to take grounded, explainable actions.

Partner with PM and design to translate messy real-world inventory problems into tractable models, and push back when "optimal" isn't what operators actually want.

Iterate with real users via design partnerships and pilot deployments. Take feedback from parts managers and procurement teams seriously and reflect it back into the model.

Contribute to the surrounding Python service: performance, observability, testing, and reliability of the inventory intelligence runtime.

Help shape how parts intelligence integrates with the broader MaintainX product over time, including learning from historical usage and purchasing data to continuously improve model inputs.

About you

5+ years of professional software engineering or data science experience, with significant time spent on optimization, forecasting, or ML systems shipped to real users.

Strong fluency with at least one optimization paradigm (LP/MILP, stochastic programming, simulation) and practical experience with demand forecasting or inventory management models.

Solid Python service engineering: APIs, async, testing, profiling, observability. You can own a production service end-to-end.

Academic grounding in Operations Research, Industrial Engineering, Supply Chain, Statistics, or a related quantitative field; strong undergraduate foundation at minimum.

Track record of iterating data-driven systems with real users — you've felt what happens when a model recommendation gets rejected and you've redesigned the approach in response.

Product mindset and delivery orientation: you ship, you measure, you iterate. You care about the operator outcome, not just the metric.

Comfort with ambiguity. You can co-design the data model and feature schema with the team rather than waiting for a clean spec.

Familiarity with GenAI tooling (LLM tool calling, structured output, prompt design for constrained generation) is expected.

Nice to have

Experience at a known product company shipping inventory management, supply chain, or procurement optimization at scale.

Exposure to learning-augmented optimization — using historical purchasing or consumption data to estimate lead times, priors, or constraint weights.

Domain experience in MRO (Maintenance, Repair & Operations) inventory, spare parts management, field service logistics, or manufacturing supply chains.

Tech-lead experience or interest in growing into a tech-lead role on this team.