About the role

Senior Applied Scientist at MaintainX enhancing scheduling systems through Python optimization and collaboration with product design teams. Focused on real-world applications of AI-driven scheduling.

Responsibilities

Own and evolve the Python optimization service that powers the Scheduling Agent, modeling, solving, and iterating on the constraint formulation as new use cases emerge.
Design and implement increasingly sophisticated scheduling capabilities: trade and crew constraints, irregular capacity patterns, production downtime windows, multi-site considerations, and reactive re-scheduling.
Build and maintain API routes and tools that expose the solver to GenAI agent workflows (tool calling, structured input/output).
Partner with PM and design to translate messy real-world scheduling problems into solver constraints, and push back when "optimal" isn't what users actually want.
Iterate the solver with real users via design partnerships and pilot deployments. Take feedback from human schedulers seriously and reflect it back into the model.
Contribute to the surrounding Python service: performance, observability, testing, and reliability of the optimization runtime.
Help shape how scheduling intelligence integrates with the broader MaintainX product over time, including learning from execution data to improve solver inputs.

5+ years of professional software engineering experience, with significant time spent on optimization, constraint programming, or operations research problems shipped to real users.
Strong fluency with CP-SAT and at least one other optimization paradigm (MILP via Gurobi/CPLEX/HiGHS, metaheuristics, or similar). You've hit the limits of one approach and made informed choices about when to use which.
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, Computer Science, or a related quantitative field, at minimum a strong undergraduate foundation; advanced degrees are common in this space but not required.
Track record of iterating optimization systems with real users, you've felt what happens when a human rejects the "optimal" answer and you've redesigned the model in response.
Product mindset and delivery orientation, you ship, you measure, you iterate. You think about the user outcome, not just the objective function.
Comfort with ambiguity. You can co-design the constraint data model 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.

Competitive salary and meaningful equity opportunities.
Healthcare, dental, and vision coverage.
401(k) / RRSP enrollment program.
Take what you need PTO.
A Work Culture where:
You'll work alongside folks across the globe that reflect the MaintainX values: Smart Humble Optimists.
We believe in meritocracy, where ideas and effort are publicly celebrated.