Senior Numerical Optimization EngineerActive$253K

Anduril Industries
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The opportunity

Maritime Digital Production (MDP) is the software and digital systems function within Anduril's Heavy Metal division. We build and deploy the full technology stack that powers Anduril's shipbuilding factories: the data infrastructure that makes every machine and sensor visible…

What you'll do

  • 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, and material availability.

  • 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 incumbent plan.

  • 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.

What they're looking for

  • 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.