What You'll DoAllocation & 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 ForCore 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 IdentityWe 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.
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.
