ML Engineer, Applied AI – Crucible

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 model [... source excerpt omitted ...] 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 AI capabilities in [... source excerpt omitted ...] o useful AI features. Set the technical direction for applied AI — Establish patterns, evaluation methods, and engineering standards for how AI is used across Crucible. Required Qualifications 5+ years building and shipping production software or ML systems, with a proven track record of success U.S. person required due to ITAR regulations Strong machine learning fundamentals, with hands-on experience beyond model API integration Demonstrated technical ownership of AI/ML systems from experimentation through production, including implementation, architecture, evaluation, and debugging Hands-on experience building production systems with LLMs or other foundation models Strong Pyt