Senior Applied Scientist, Scheduling and Optimization

MaintainX is the world's leading AI-powered maintenance and asset management platform, serving 13,000+ customers including Duracell, Shell, Cintas, and Brenntag. We raised $150M in Series D funding led by Bessemer Venture Partners and Bain Capital Ventures, bringing our total funding to $254M. We were named to the Forbes 2025 Cloud 100 , the definitive ranking of the top 100 private cloud companies in the world. We're growing fast and hiring the engineering talent to match. We're looking for a Senior Software Engineer to own the constraint-solving engine behind our Scheduling Agent, one of the most strategic bets on our Planning & Scheduling roadmap. The engine is a Python service built on CP-SAT that takes work orders, technicians, and a mix of hard and soft constraints, and produces a feasible weekly schedule that enterprise maintenance teams can trust. Your focus will be evolving the solver to handle increasingly complex, real-world scheduling scenarios and exposing it as tools for GenAI agent workflows. This is a high-ownership role. You'll have the space to shape the modeling approach, partner closely with product and design on what scheduler users actually need, and ship iteratively against feedback from real enterprise customers. What you'll do 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. About you 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. Nice to have 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 sch