Firestorm Labs - Democratize the fight with radically-affordable, mission-adaptable power for air, land, and sea. Backed by NEA.
About the role
Firestorm's manufacturing platform has an intelligence layer - a planning workbench, a simulation engine, an analytics surface, and eventually an AI assistant - that turns operational data into better decisions. Building this layer requires applied math, pragmatic ML systems engineering, and a deep understanding of the manufacturing domain it serves. As an Applied AI & Optimization Engineer, you will own the algorithms and systems behind this intelligence layer.
What they're looking for
- 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
More about this role
Who We Are
Firestorm is building the next generation of uncrewed aircraft and the advanced manufacturing systems that deliver them at speed. The Software Integration & Operations department owns the software layer that spans factory floor to cloud - the applications, automation, edge systems, and intelligence that make it possible to iterate product designs, automate advanced manufacturing, and scale production with uncompromising quality and rigor.
Firestorm's manufacturing platform has an intelligence layer - a planning workbench, a simulation engine, an analytics surface, and eventually an AI assistant - that turns operational data into better decisions. Building this layer requires applied math, pragmatic ML systems engineering, and a deep understanding of the manufacturing domain it serves. As an Applied AI & Optimization Engineer, you will own the algorithms and systems behind this intelligence layer. Near-term, your focus is the planning workbench and simulation engine: optimization algorithms for work order scheduling, resource allocation, and conflict resolution. Longer-term, you will lead integration of open-source and custom LLMs into the platform's AI assistant -...
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