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.
Qualification
Solid Python service engineeringComfort with ambiguityDomain experience in MRO (Maintenance
Required
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.
Preferred
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.
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.
