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Gallatin is rebuilding logistics infrastructure for U.S. national security and allied partners, building AI systems that drive logistics decision-making from factory to field. This role focuses on allocation and packing systems—the critical layer where data becomes actionable decisions.
You will own the full lifecycle of packing and allocation modeling: building and maintaining models for transport assets, encoding constraints around volume, weight, compatibility, sequencing, and asset usage, and balancing packing efficiency against runtime and operational feasibility. You'll develop and refine heuristics and exact methods for solving complex packing and allocation problems at scale, evaluating tradeoffs between optimality, speed, and explainability.
Data ownership is central to this role. You'll take responsibility for the data inputs feeding allocation and packing systems—asset and supply attributes, validation, normalization, and maintenance of packing datasets. You'll manage edge cases and incomplete data directly, ensuring data quality end-to-end.
Production integration is equally important. You'll integrate packing outputs into resupply and routing workflows, collaborate with other teams to confirm physical executability, validate solutions through scenario testing and operational feedback, and ensure AI-generated plans enforce physical feasibility and constraint layers.
Gallatin values clear thinking, direct communication, and ownership that doesn't stop until something works. The mission is urgent: enabling faster, smarter logistics decisions in contested environments where the stakes are real.
REQUIREMENTS:
Core Skills:
- Strong programming in Python or similar, with ability to translate allocation logic into deterministic, testable production code
- Strong foundation in operations research, optimization, or applied algorithms for resource allocation and physical feasibility
Background (one or more of the following):
- Operations research, applied math, industrial engineering, or related fields
- Experience implementing allocation and assignment algorithms (matching, prioritization, constraint-based allocation)
- Experience modeling and solving packing problems (bin packing, knapsack, multidimensional 2D/3D packing)
- Experience encoding capacity, compatibility, priority, and physical constraints in allocation and packing systems
- Familiarity with optimization techniques (linear programming, mixed-integer programming, heuristic and approximation methods for NP-hard problems)
- Experience balancing solution quality, feasibility, and computational performance in large-scale or time-sensitive systems
- Experience with vehicle loading or palletization problems
Systems Thinking:
- Ability to reason about physical constraints and edge cases
- Comfort owning data pipelines and assumptions end-to-end
- Strong attention to correctness and failure modes
Bonus:
- Experience integrating packing with simulation systems
- Prior exposure to defense or government planning environments
- Experience with machine learning models, experimentation (A/B testing), and causal inference
Note: U.S. citizenship required. Position may require ability to obtain and maintain a U.S. government security clearance.
