Senior Numerical Optimization Engineer (Arsenal 2)
Santa Ana · Santa Ana, CA (OC-42) · USD 191,000-253,000 · 5+ years experience · Full-time · Posted Oct 15, 2026
At a glance
- Experience
- 5+ years experience
- Employment
- Full-time
- Location
- Santa Ana · Santa Ana, CA (OC-42)
- Salary
- USD 191,000-253,000 · Base salary
About the role
- As a Senior Numerical Optimization Engineer at Anduril Industries, you will own the scheduling optimization core that decides when and how ships get built within the Maritime Digital Production team.
- This role involves formulating shipyard production scheduling as a mathematical optimization problem for a graph of 100,000+ manufacturing operations and building the solvers that continuously replan as the factory changes.
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 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 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.
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.
- 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.
Preferred qualifications
- 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 and decomposition methods.
- 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.
Benefits
- Highly competitive equity grants included in the majority of full time offers
- Comprehensive, competitive benefits package available at little to no cost to employees supporting health, recovery, and whatever comes next
Skills
- Python
- NumPy
- SciPy
- Pandas
- Gurobi
- CPLEX
- OR-Tools
- SCIP
- PuLP
- CVXPY
- Pyomo
- C++
Show all 13 skills
- Rust
