Machine Learning Engineer, Applied AI

🏢Who We Are

At Firestorm, we are building the future of expeditionary defense manufacturing and autonomous systems. Modern conflict has exposed a fundamental problem: the systems needed most by operators are often too expensive, too slow to produce, and too difficult to sustain at scale. Firestorm exists to change that.

We develop mission-adaptable aerial systems and deployable manufacturing infrastructure designed to put capability directly into the hands of the warfighter. From modular unmanned aircraft to xCell — our deployable microfactory — our goal is to make defense systems rapidly deployable, adaptable, and producible at the point of need.

We are looking for builders, operators, and problem-solvers who want to work on meaningful technology with real-world impact.

🏢About the Role

Firestorm builds uncrewed aircraft and the manufacturing network that produces them: full-capability plants, deployable xCell edge factories, and forward-deployed sites that stand up production near the point of need.

Crucible is the software that runs that network. It combines production planning, execution, supply chain, and manufacturing data into a common model of how production operates.

We are hiring a Staff Machine Learning Engineer to build the applied AI systems behind Crucible. You will work alongside data science and software engineering to productionize optimization and ML models, integrate foundation models into the platform, and build AI-assisted workflows grounded in Crucible's manufacturing data and system model.

This is a hands-on engineering role, not a research position. We are looking for someone who has shipped ML and AI capabilities into real products and understands what it takes to make them reliable, measurable, and useful after the prototype works.

What You’ll Do

• 🔹Build AI capabilities in Crucible — Develop AI-assisted workflows that use Crucible's data, APIs, and domain model to surface insights, answer questions, and help users make decisions.
• 🔹Productionize decision models — Work with data science to integrate optimization, forecasting, and other models into reliable product capabilities.
• 🔹Build AI systems end-to-end — Design model interfaces, retrieval, tool use, orchestration, evaluation, and the services required to run them in production.
• 🔹Select and evaluate models — Choose models and approaches based on capability, reliability, latency, security, and deployment constraints.
• 🔹Build for constrained environments — Support…