Job description

ABOUT GALLATIN At Gallatin, we are rebuilding logistics infrastructure for the national security missions of the United States and allied partners. We build AI systems that determine how logistics decisions are made — not just how they're executed. From factory to foxhole, we operate at the layer where data becomes decisions, and decisions make the advantage. WHAT YOU'LL DO ALLOCATION & PACKING MODELING - Build, maintain, and implement packing and allocation models for transport assets. - Encode constraints around volume, weight, compatibility, sequencing, and asset usage. - Weigh packing efficiency against runtime and real-world operational constraints. ALGORITHM ENGINEERING - Develop and refine heuristics or exact methods for solving packing and allocation problems. - Scale packing solutions across large fleets and dynamic inputs. - Evaluate tradeoffs between optimality, speed, and explainability. DATA OWNERSHIP & PREPARATION - Take ownership of the data inputs that feed allocation and packing - asset and supply attributes included. - Validate, normalize, and maintain packing-related datasets. - Manage edge cases and incomplete data directly. PRODUCTION INTEGRATION - Integrate packing outputs into resupply and routing workflows. - Collaborate with other teams to confirm physical executability of outputs. - Validate solutions through scenario testing and operational feedback. - Ensure AI-generated plans cannot bypass physical feasibility or constraint enforcement layers. WHAT WE’RE LOOKING FOR CORE SKILLS - Strong programming skills in Python or similar, with experience translating allocation logic into deterministic, testable production code. - Strong foundation in operations research, optimization, or applied algorithms for resource allocation and physical feasibility challenges. OR BACKGROUND - Background in operations research, applied math, industrial engineering, or related fields. - Familiarity implementing allocation and assignment algorithms, including matching, prioritization, and constraint-based allocation under competing demands. - Experience modeling and solving packing problems, such as bin packing, knapsack, or multidimensional (2D/3D) packing problems. - Experience encoding capacity, compatibility, priority, and physical constraints in allocation and packing systems. - Familiarity with optimization techniques such as linear programming, mixed-integer programming, or 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 POINTS - Experience integrating packing with simulation systems. - Prior exposure to defense or government planning environments. - Experience with Machine Learning models, experimentation (e.g., A/B testing) and causal inference MISSION AND IDENTITY We are building the system that enables faster, smarter logistics decisions in contested environments, and we're doing it with a team of seasoned entrepreneurs, operators, and technologists who have built and scaled solutions in this space before. We hold ourselves to an extremely high standard. We value clear thinking, direct communication, and the kind of ownership that doesn't stop until something actually works. Our mission is to create decision advantage when the stakes are the highest. If we succeed, the system doesn't just run; it gets smarter. We're not building AI for its own sake. We're building it because faster, smarter decisions in the most demanding environments on earth can't wait. If you want to work somewhere the stakes are real and the mission is urgent — you'll fit in here. WHY GALLATIN? The logistics infrastructure that supports America's warfighters and humanitarian disaster responders is overdue for transformation, and we are building it. From defense operations to disaster response, we're solving the hardest problems that keep missions moving when it matters most. Join a team where the mission is the point. Compensation: Gallatin offers competitive compensation commensurate with experience. Actual compensation may vary based on experience, skills, and location. In addition to base salary, we offer a generous equity grant, full healthcare coverage, 401k, unlimited PTO, and the perks of working in a high-caliber, mission-driven environment. Gallatin is an equal opportunity employer. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity, or any other characteristic protected by applicable federal, state, or local law. This position may require the ability to obtain and maintain a U.S. government security clearance. The successful candidate must be able to work in a classified environment when necessary. We comply with the United States Department of Labor's Pay Transparency provision dol.gov. Note: Due to the nature of certain government contracts held by this organization, U.S. citizenship is a requirement for all positions at Gallatin. Proof of citizenship will be required prior to employment if selected.

Requirements

Must-haves

Build, maintain, and implement packing and allocation models for transport assets.
Encode constraints around volume, weight, compatibility, sequencing, and asset usage.
Weigh packing efficiency against runtime and real-world operational constraints.
Develop and refine heuristics or exact methods for solving packing and allocation problems.
Scale packing solutions across large fleets and dynamic inputs.
Evaluate tradeoffs between optimality, speed, and explainability.
Take ownership of the data inputs that feed allocation and packing - asset and supply attributes included.
Validate, normalize, and maintain packing-related datasets.
Manage edge cases and incomplete data directly.
Integrate packing outputs into resupply and routing workflows.
Collaborate with other teams to confirm physical executability of outputs.
Validate solutions through scenario testing and operational feedback.
Ensure AI-generated plans cannot bypass physical feasibility or constraint enforcement layers.
Strong programming skills in Python or similar, with experience translating allocation logic into deterministic, testable production code.
Strong foundation in operations research, optimization, or applied algorithms for resource allocation and physical feasibility challenges.
Background in operations research, applied math, industrial engineering, or related fields.
Familiarity implementing allocation and assignment algorithms, including matching, prioritization, and constraint-based allocation under competing demands.
Experience modeling and solving packing problems, such as bin packing, knapsack, or multidimensional (2D/3D) packing problems.
Experience encoding capacity, compatibility, priority, and physical constraints in allocation and packing systems.
Familiarity with optimization techniques such as linear programming, mixed-integer programming, or heuristic and approximation methods for NP-hard problems.
Experience balancing solution quality, feasibility, and computational performance in large-scale or time-sensitive systems.

Nice-to-haves

Experience integrating packing with simulation systems.
Prior exposure to defense or government planning environments.
Experience with Machine Learning models, experimentation (e.g., A/B testing) and causal inference

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aipythonmachine learning

Platform: ashby
Effort: quick

Benefits

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Vision

401k

Pto