ML Engineer, Applied AI – Crucible at Firestorm in San Diego, CA

Job description

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 AI capabilities in cloud, air-gapped, and edge deployments where external model APIs may not be available. Ground AI in real operations — Work with manufacturing, supply chain, and product teams to turn operational problems into 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 Python and software engineering fundamentals Experience with modern ML frameworks and transformer-based models Experience designing evaluations and monitoring production AI/ML behavior Demonstrated ability to make sound technical tradeoffs across model quality, reliability, latency, cost, and system complexity Preferred Qualifications Experience working with open-source models and local model inference Experience with manufacturing, supply chain, logistics, or other operational software Experience with model fine-tuning, inference optimization, or GPU deployment Aerospace, defense, robotics, or other complex p