Applied AI & Optimization Engineer

About the role

The Applied AI & Optimization Engineer will own the algorithms and systems behind the planning workbench and simulation engine, focusing on optimization algorithms for work order scheduling, resource allocation, and conflict resolution. Long-term, they will lead the integration of open-source and custom Large Language Models (LLMs) into the platform's AI assistant, including air-gapped and on-edge deployments for DoD contexts.

Responsibilities

• Own the optimization algorithms behind the planning workbench and simulation engine - scheduling, resource allocation, constraint satisfaction, conflict detection.
• Design and implement the analytics layer of the platform: defect trends, yield analytics, throughput modeling, and operational intelligence.
• Lead the platform's AI assistant integration: selecting, evaluating, deploying, and fine-tuning open-source or custom LLMs for cloud, air-gapped, and on-edge contexts.
• Productionize optimization and ML systems in partnership with full-stack and infrastructure engineers - reliable services the platform depends on, not prototypes.
• Partner with domain experts in manufacturing engineering, quality, and planning to ground models and algorithms in real operational constraints.
• Evaluate and advocate for build-vs-buy decisions across optimization libraries, ML tooling, and model vendors.

Requirements

• Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent practical experience)
• U.S. Citizenship required due to ITAR regulations
• 5+ years of engineering experience with substantial applied optimization, operations research, or ML systems work
• Deep proficiency in Python; fluency with at least one optimization framework - MILP solvers, constraint solvers, OR-Tools, or equivalent
• Track record of productionizing algorithmic systems - you have shipped optimization or ML into real users' hands, not just research artifacts
• Strong applied math foundation: combinatorial optimization, heuristics, or statistical modeling relevant to scheduling and resource allocation problems
• Demonstrated ability to partner with domain experts and translate operational constraints into model formulations
• Demonstrated history of holding yourself and your teammates to a high standard, even when it creates discomfort

Preferred Qualifications

• Prior experience building scheduling, planning, or resource allocation systems for manufacturing, logistics, or similar domains
• Hands-on experience deploying or fine-tuning open-source LLMs (Llama, Mistral, or similar) for constrained environments
• Background with air-gapped or on-edge model deployment
• Familiarity with discrete-event simulation or agent-based modeling
• Prior experience in defense, aerospace, or regulated industry applications