Ph.D. in Operations Research, Applied Mathematics, or a closely related field, with publications or production work in scheduling or large-scale optimization.
Experience with resource-constrained project scheduling (RCPSP), job shop scheduling, or precedence-constrained scheduling in a production setting.
Experience with dynamic or online rescheduling: replanning under uncertainty, schedule stability, and rolling-horizon methods.
Experience benchmarking solvers or algorithms where no ground-truth optimum exists, using dual bounds, anytime curves, or ablation across constraint classes.
Experience with metaheuristics (genetic algorithms, simulated annealing, tabu search, large neighborhood search) and decomposition methods (Benders, column generation, Lagrangian relaxation).
Experience with performance-critical implementation in C++ or Rust for optimization kernels or hot-path graph traversal.
Familiarity with graph databases or large in-memory graph representations at 100K+ nodes.
Experience in manufacturing, industrial, or OT-adjacent domains, and familiarity with BOM structures, manufacturing routing, work centers, and MES, ERP, PLM, or APS systems.
Exposure to traditional and non-traditional optimization hardware e.g. GPU-accelerated solvers, HPC, or quantum annealing and QUBO formulations.
Experience in hyper-growth startup-like environments, with demonstrated success balancing speed, ambiguity, and long-term system health.
Familiarity with frontier AI tooling, AI coding assistants, and AI-enabled software development workflows.
Experience in shipbuilding, aerospace, or complex discrete manufacturing.
Eligible to obtain and maintain a U.S. Secret security clearance.