The ideal candidate possesses deep expertise in classical optimization algorithms, robust Python programming skills, and a solid foundation in data modeling. Experience with developing hybrid quantum optimization solutions is a plus.

You will leverage state-of-the-art, GenAI-powered development tools such as Claude Code to accelerate solution development and enhance our optimization software. This role demands creative problem-solving, a self-starter mentality, and the ability to rapidly apply algorithmic theory and mathematic modeling to practical, real-world optimization challenges.

You will be designing, implementing, and deploying optimization algorithms and services that integrate seamlessly into larger defense systems, working across various platforms (on-prem, cloud, and hybrid quantum computing environments).

Familiarity with modeling linear and non-linear optimization problems, rapid prototyping, integrating optimization solutions into existing architectures, leveraging APIs, and utilizing open-source tools will be crucial.

If you thrive in a dynamic environment that values creative problem-solving, love writing code, excel as both an individual contributor and team player, are eager to learn, and bring a can-do attitude, this role is for you.

Key Responsibilities:

• Design, develop, and implement highly efficient optimization algorithms and software solutions to solve challenging problems in areas such as resource allocation, scheduling, routing, mission planning, control systems, and supply chain logistics.

• Apply classical optimization techniques (e.g., linear programming, mixed-integer linear programming, combinatorial optimization, network flow, dynamic programming, heuristics, metaheuristics) to model and explore novel approaches.

• Utilize GenAI tools (e.g., OpenAI Codes, Claude Code, GitHub Copilot) to rapidly prototype, refine, and test algorithmic solutions, improving development velocity and code quality.

• Develop robust data models and efficient data pipelines to support complex optimization problems, ensuring data integrity and efficient processing for algorithmic inputs and outputs.

• Collaborate with multidisciplinary teams (software engineers, data scientists, domain experts, product managers) to integrate optimization engines and services into larger defense systems and platforms.

• Perform rigorous testing, validation, and performance analysis of optimization solutions, ensuring scalability, reliability, and accuracy under diverse operational conditions.

• Participate actively in the entire Software Development Lifecycle (SDLC) from requirements gathering and design to deployment, monitoring, and maintenance.

• Support Anduril- and customer-funded R&D efforts, contributing to technical documentation, presentations, and patent applications.

Requirements:

• Bachelor's degree in Computer Science, Software Engineering, Applied Mathematics, Operations Research, or a related quantitative field.

• 3+ years of professional experience in software development with a dedicated focus on optimization, algorithmic problem-solving, or operations research.

• Experience solving optimization problems in defense, transportation, supply chain, logistics, network optimization, smart grids or similar.

• Expert proficiency in Python for scientific computing and robust software development.

• Strong theoretical and practical understanding of classical optimization algorithms (e.g., linear programming, mixed-integer linear programming, constraint programming, network flow, dynamic programming, heuristics, meta heuristics).

• Hands-on experience with optimization libraries and commercial/open-source solvers (e.g., SciPy Optimize, PuLP, CVXPY, Gurobi, CPLEX, OR-Tools, GEKKO).

• Solid experience with data modeling, data structures, and algorithms to efficiently prepare, process, and manage data for optimization problems.

• Demonstrable hands-on experience using GenAI tools (e.g., OpenAI Codex, Claude Code, Gemini Code Assist, GitHub Copilot, Amazon CodeWhisperer, or similar) for software development, code generation, debugging, and algorithmic exploration.

• Proficiency in using numerical computing libraries such as NumPy, SciPy, and Pandas.

• Demonstrated understanding and application of software testing principles and practices, including unit testing, integration testing, and end-to-end testing.

• Ability to develop, test, and deploy software effectively on Linux-based systems.

• Eligible to obtain and maintain an active U.S. Top Secret SCI security clearance.

• Experience with Git version control, build tools, and CI/CD pipelines.

• Strong problem-solving skills, meticulous attention to detail, and the ability to work effectively in a collaborative team environment.

• Excellent communication and interpersonal skills, with the ability to effectively articulate complex technical concepts to diverse audiences.

Preferred Qualifications:

• Master's or Ph.D. in Computer Science, Applied Mathematics, Operations Research, or a closely related quantitative field.

• Familiarity with or a strong interest in quantum optimization algorithms, quantum computing concepts, or quantum-inspired heuristic approaches.

• Experience with D-Wave’s quantum annealing platform is a plus.

• Experience with performance-critical programming languages such as C++ or Java.

• Experience with cloud platforms (e.g., AWS, Azure, GCP) for deploying scalable optimization solutions or high-performance computing (HPC) environments.

• Prior experience in defense, aerospace, logistics, supply chain management, robotics, or manufacturing optimization domains.

• Familiarity with integrating machine learning models with optimization techniques (e.g., prescriptive analytics, reinforcement learning for optimization).

• Excellent communication skills with the ability to articulate complex technical concepts, present findings, and influence technical direction across diverse teams.

• Willingness to travel up to approximately 10%.

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