Senior Numerical Optimization Engineer

Job description

Job responsibilities

• Formulate shipyard production scheduling as a mathematical optimization problem covering precedence with lag, disjunctive spatial exclusion, multi-resource capacity with qualification matching, calendars and shifts,…
• Own the solver-agnostic scheduling interface, integrate commercial and open-source solvers behind it, and benchmark them against each other on makespan, stability, resource utilization, and solve time.
• Build the two-tier replan path: fast local repair that returns a feasible schedule in seconds for a bounded affected subgraph, and background global re-solve that runs for minutes and swaps in when it beats the…
• Develop the stability objective that keeps a replan from needlessly moving work the factory has already staged, and the policy that decides when a better plan is worth the churn.
• Establish what "good enough" means when there is no ground truth: LP relaxation lower bounds, best-of-N ensemble upper bounds, quality ratios, and solution-quality regression tests that run on every change.
• Research and evaluate approaches beyond the V1 solver, including decomposition, metaheuristics, rolling-horizon methods, and non-traditional hardware (wafer-scale compute, quantum annealing) where they earn their place.
• Turn disruption events into replans: determine blast radius across the operation graph, scope the re-solve, merge the result into the live schedule, and resolve boundary conflicts.
• Build adaptive planning templates that generate recovery work (E.g. a damaged part that must be removed, replaced, and reinspected) by composing geometric queries with scheduling logic.

Job requirements

• 5+ years of experience building production software systems, ideally in a fast-paced environment.
• Deep expertise in numerical optimization, including linear programming, mixed-integer linear programming, constraint programming, combinatorial optimization, or metaheuristics.
• Demonstrated ability to take a real-world problem, formulate it as a mathematical model, and carry that model all the way into a system other people depend on.
• Hands-on experience with optimization solvers and libraries (E.g. Gurobi, CPLEX, OR-Tools, SCIP, PuLP, CVXPY, Pyomo), including the judgment to recognize when a given solver or formulation is the wrong tool.
• Expert proficiency in Python for scientific computing and robust software development, with strong foundations in numerical computing libraries (NumPy, SciPy, Pandas).
• Strong theoretical and practical grounding in graph algorithms, constraint satisfaction, and computational complexity analysis.
• Experience with scheduling or resource allocation at a scale where exact methods stop working, and the judgment to know when to stop optimizing.
• Experience building systems that operate reliably under real operational constraints such as high availability, latency budgets, or degraded inputs.