Senior Numerical Optimization Engineer (Arsenal 2)

Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology.

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.

As a Senior Numerical Optimization Engineer on this unique initiative, you will own the scheduling optimization core that decides when and how ships get built.

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, 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.
• 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.
• Compose scheduled operations into work orders a manufacturing execution system can dispatch, and recompose them when the plan changes.
• Write production-quality code with comprehensive tests, participate in code review and architectural discussion, and instrument your systems so solution quality is observable, not just uptime.
• Partner with manufacturing engineers, planners, and factory operators to turn how the yard actually works into constraints and objectives.
• Work alongside the computational geometry engineer who derives geometric precedence from CAD, and the data engineers who supply the operation graph and the live factory event stream.
• Leverage AI tooling (coding assistants, automation) in your development workflow and contribute to team engineering practices.
• Join an on-call rotation supporting production scheduling systems.

Required Qualifications

• 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.
• Excellent written and verbal communication, including the ability to explain an optimization trade-off to a factory planner who does not care about duality gaps.
• Advanced degree in Operations Research, Applied Mathematics, Computer Science, Industrial Engineering, or a related quantitative field, or equivalent depth built in practice.
• U.S. Person status is required as this position needs to access export controlled data.