Gallatin AI
AI Engineer - Allocation and Packing Systems
About this role
Gallatin seeks an AI Engineer to design and optimize allocation and packing algorithms for military logistics operations. You'll develop constraint-based models for transport asset utilization, implement scalable optimization solutions, and integrate them into production resupply workflows while ensuring physical feasibility.
What you'll do
- Design packing and allocation models encoding volume, weight, compatibility, and sequencing constraints
- Implement and tune optimization algorithms balancing efficiency, speed, and explainability
- Own data pipelines for asset and supply properties, including validation and edge case handling
- Integrate packing solutions into resupply and routing systems with operational partners
- Validate solutions through scenario testing and feedback to ensure physical executability
- Scale solutions across large fleets and dynamic inputs
What they're looking for
- Python or equivalent production-grade programming
- Operations research and optimization fundamentals
- Packing problem modeling (bin packing, knapsack, 2D/3D)
- Linear programming, mixed-integer programming, or heuristic methods
- Constraint encoding and satisfaction
- Systems thinking and physical constraint reasoning
- Data pipeline ownership and validation
- Vehicle loading or palletization problem experience
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Gallatin AI
Gallatin AI builds AI-powered logistics optimization systems for national security and defense operations, focusing on feasibility validation, allocation algorithms, routing networks, and supply chain decision-making. The company is hiring AI Engineers, Backend Engineers, and Infrastructure Engineers to design scalable, secure systems that ensure military logistics plans are compliant, auditable, and operationally robust.
View all jobs at Gallatin AILikely interview questions
- Walk us through a packing or allocation problem you've solved—what constraints did you encode, and how did you balance optimality against runtime?
- Describe your experience with optimization techniques like linear programming, mixed-integer programming, or heuristic methods. Which have you applied and why?