This role pays more than
75%
of similar roles. Most pay
$166,651–$222,000
— the shaded band above.
At the midpoint, this role pays about
$222k
versus about
$194k
for comparable roles.
Based on 240 similar postings.
Employer
About Anduril Industries
Anduril Industries is a defense technology company that builds advanced hardware and software systems for national security, including autonomous drones, surveillance systems, and the Lattice AI command platform.
Anduril Industries currently has
1697 open roles
on FindRole.
Listed pay typically runs
$146,000–$194,000
across 1504 roles with salary data.
As a Senior Numerical Optimization Engineer on the Maritime Digital Production team, you will own the scheduling optimization core for shipyard manufacturing. You will model complex production environments as mathematical problems involving precedence, spatial constraints, and resource capacity to automate how ships are built. Your daily work involves formulating these problems, building and benchmarking solver-agnostic interfaces using tools like Gurobi, CPLEX, OR-Tools, SCIP, PuLP, or CVXPY, and developing two-tier replan paths for real-time disruptions. You will utilize Python with NumPy, SciPy, and Pandas to develop robust software that manages over 100,000 manufacturing operations. This role addresses the NP-hard challenge of resource-constrained project scheduling in a high-stakes industrial environment where production must remain stable despite constant changes. You will collaborate with engineers to translate physical shipyard workflows into technical constraints for automated systems.
Formulate shipyard production scheduling as complex mathematical optimization problems involving spatial constraints, resource capacity, and material availability.
Build and benchmark a solver-agnostic interface to evaluate various commercial and open-source solvers for speed and stability.
Develop a two-tier replanning system providing both immediate local repairs and background global re-solves for production disruptions.
Create stability objectives and metrics to ensure that automated schedule changes do not cause unnecessary churn on the shop floor.
Research and implement advanced optimization techniques such as metaheuristics, decomposition methods, and non-traditional hardware solutions.
Translate real-world manufacturing constraints into technical requirements by collaborating with engineers and factory operators.
Write production-quality code for the scheduling core while ensuring system performance is observable through rigorous testing and instrumentation.
Participate in an on-call rotation to support critical production scheduling systems in a high-stakes environment.
What we're looking for
Must have at least 5 years of experience building production software systems.
Requires deep expertise in numerical optimization including linear programming, mixed-integer linear programming, constraint programming, or metaheuristics.
Must demonstrate the ability to formulate real-world problems into mathematical models and implement them as production systems.
Requires hands-on experience with optimization solvers and libraries such as Gurobi, CPLEX, OR-Tools, SCIP, PuLP, CVXPY, or Pyomo.
Requires expert proficiency in Python for scientific computing and knowledge of NumPy, SciPy, and Pandas.
Must have a strong theoretical and practical grounding in graph algorithms, constraint satisfaction, and computational complexity analysis.
Requires an advanced degree in Operations Research, Applied Mathematics, Computer Science, Industrial Engineering, or a related quantitative field.
Must have U.S. Person status to access export-controlled data.
Machine Learning
Operations Research
Python
SQL
Pandas
scikit-learn
XGBoost
Keras
TensorFlow
Deep Learning
Combinatorial Optimization
Mathematical Programming
Data Manipulation
Algorithms