Senior Applied Scientist, Scheduling and Optimization

MaintainX

21h•Remote

About The Position

MaintainX is seeking a Senior Software Engineer to lead the constraint-solving engine for their Scheduling Agent. This role involves owning and evolving a Python service built on CP-SAT, which generates feasible weekly schedules based on work orders, technicians, and various constraints. The focus will be on enhancing the solver to handle complex real-world scheduling scenarios and integrating it with GenAI agent workflows. This is a high-ownership position where the engineer will shape the modeling approach, collaborate with product and design teams, and deliver iteratively based on customer feedback.

Requirements

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).
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.

Nice To Haves

Experience at a known, reputable product company shipping optimization or scheduling products at scale.
Exposure to learning-augmented optimization, using historical execution data to estimate durations, priors, or constraint weights.
Domain experience in scheduling, workforce management, field service, manufacturing, logistics, or similar resource-constrained planning problems.
Tech-lead experience or interest in growing into a tech-lead role on this team.

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.