AI Engineer: Decision & Optimization Systems (Equity)

Gallatin AI, Inc.; El Segundo (CA); On-site; USD 80,000 - 210,000; Full time

Benefits offered by this job

Equity grant

Full healthcare coverage

401k

Unlimited PTO

Job summary

Gallatin AI, Inc. seeks an AI Engineer – Decision & Optimization Systems to own feasibility and authority layers between AI reasoning and real-world execution. You will build systems that ensure plans respect policy, safety, and operational realities before any action is taken.

You’ll collaborate with routing, packing, optimization, and AI-agent teams to turn intent and uncertainty into auditable, explainable decision systems used in high-stakes logistics environments.

Qualifications

• Proficiency in Python, Java, Go or C++ and production-grade software.
• Experience building constraint-based or rule-based systems in production.
• Experience integrating AI or agent-based reasoning into downstream decision or execution pipelines.
• Strong understanding of optimization methods and decision science.

Responsibilities

• Design and implement feasibility and authority models that encode policy rules.
• Build and maintain a centralized constraint framework across planning and optimization systems.
• Surface diagnostics for infeasible plans and explainable results.
• Integrate feasibility checks into real-time and batch planning pipelines.
• Validate system behavior through simulations and operational feedback.

Skills

Python/Java/Go/C++

Constraint-based systems

AI/agent-based integration

Systems thinking

Education

Operations Research / Applied Math / Industrial Eng / CS

Tools

Gurobi

CPLEX

OR-Tools

Pyomo

AMPL

Job description

Gallatin AI, Inc. seeks an AI Engineer – Decision & Optimization Systems to own feasibility and authority layers between AI reasoning and real-world execution. You will build systems that ensure plans respect policy, safety, and operational realities before any action is taken.

You’ll collaborate with routing, packing, optimization, and AI-agent teams to turn intent and uncertainty into auditable, explainable decision systems used in high-stakes logistics environments.