AI Engineer – Decision & Optimization Systems

Gallatin•El Segundo, CA

1d•$80,000 - $210,000•Onsite

About The Position

Gallatin is seeking an AI Engineer – Decision & Optimization Systems to develop and manage the feasibility, authority, and constraint layers that bridge AI reasoning and real-world execution in logistics. This role involves building systems that ensure AI-generated logistics plans adhere to command hierarchy, policy, safety, and operational realities before any optimizer, agent, or human can act. The engineer will collaborate with routing, packing, optimization, and AI-agent teams to transform intent, authority, and uncertainty into auditable, enforceable, and explainable decision systems. The work is critical for ensuring that AI-driven logistics plans are not only efficient but also compliant and safe, especially in high-stakes national security and humanitarian missions.

Requirements

Proficiency in at least one of: Python, Java, Go, or C++
Experience building constraint-based or rule-based systems in production.
Experience integrating AI or agent-based reasoning into downstream decision or execution pipelines.
Degree or equivalent experience in Operations Research, Applied Math, Industrial Engineering, Computer Science, or related field.
Strong understanding of: Linear programming, Mixed-integer programming, Constraint satisfaction systems.
Experience using tools such as Gurobi, CPLEX, OR-Tools, Pyomo, AMPL, or equivalent.
Ability to reason about feasibility vs optimality tradeoffs.
Comfort integrating probabilistic or AI-derived outputs into deterministic systems.
Strong instincts for debugging, failure analysis, and explainability in high-stakes environments.

Nice To Haves

Experience with logistics, supply chain, or routing systems.
Experience working alongside optimization or planning teams.
Exposure to defense, government, or mission-critical operations.
Prior work integrating AI agents into decision or execution pipelines.

Responsibilities

Design and implement models that encode command hierarchy, authority limits, and policy rules into formal feasibility checks.
Convert outputs from AI agents (LLMs, planners, probabilistic models) into deterministic, enforceable constraints before execution.
Ensure authority and policy interpretations are traceable, inspectable, and safe.
Build and maintain a centralized constraint framework used across planning and optimization systems.
Encode authority rules, timing windows, compatibility constraints, and asset availability/readiness.
Provide clear diagnostics for infeasible plans and constraint violations for both humans and machines.
Own the data inputs needed for feasibility and constraint enforcement.
Define and enforce data contracts, normalization, and validation pipelines.
Build robustness against incomplete, delayed, or noisy operational data.
Integrate feasibility checks into real-time and batch planning pipelines.
Partner with optimization and execution teams to gate every action on valid, policy-compliant inputs.
Validate system behavior through scenario testing, simulations, and operational feedback from real users.