Senior AI Platform Engineer

Who We Are
At Firestorm, we’re on a mission to revolutionize how defense solutions are designed and delivered. Our goal is to empower U.S. ally nations to effectively deter aggressors — regardless of their defense budget — through innovative, cost-efficient technologies. We call this vision “democratized deterrence.” As a VC-backed company at the intersection of defense and Silicon Valley, we’re pioneering the development of mission-adaptable aerial vehicles that put power back into the hands of operators. By prioritizing operator effectiveness, we’re pioneering a new era of aerial vehicle design. We aim to upend the traditional defense procurement model by delivering world-class capabilities at a fraction of the usual cost. Join us at Firestorm as we redefine defense procurement, making cutting-edge technology accessible to all at a fraction of the cost. About the Role At Firestorm, we are building autonomous aerial systems that operate where they are needed most, when they are needed most. Our mission requires speed, ingenuity, and a relentless commitment to engineering excellence. We move fast, test constantly, and deliver capability that performs in the real world, not just in simulation. 
We are looking for a Senior AI Platform Engineer who is excited to build the platform foundations that make AI-enabled software reliable, secure, and operable at scale. You will design and implement core services, registries, workflow orchestration primitives, and the integration patterns that connect AI-driven workflows to internal systems, without compromising governance, auditability, or safety. This is a hands-on role with significant ownership: you will build the primitives that other engineers depend on, and you will help set the standard for reliability and software quality in a fast-moving environment. 
If you want to build systems that matter, own your work end to end, and be part of a team that values bold thinking grounded in rigorous engineering, Firestorm is the place to do it. What You'll Do

• Build and operate core backend services that power AI-enabled workflows (APIs, orchestration, storage, and internal integrations) 
• Design scalable data models and registries for versioned artifacts and metadata, with strong traceability and auditability 
• Implement secure-by-default service patterns: authN/authZ, audit logs, secrets handling, and least-privilege access 
• Build reliability foundations: observability, metrics, tracing, alerting, SLOs, incident response playbooks 
• Implement idempotent APIs and state-handling patterns for resilient workflows (retries, partial failure, reconciliation) 
• Create integration adapters and event-driven plumbing to safely connect workflows to internal systems 
• Establish release and deployment practices: CI/CD pipelines, environment promotion, rollback strategies, and safe migrations 
• Partner closely with AI and application engineers to define interfaces, validation layers, and operational constraints 
• Identify performance, scalability, and security risks early and ship pragmatic solutions quickly 

Minimum Qualifications 

• Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent practical experience) 
• U.S. Citizenship and the ability to obtain and maintain a U.S. Government security clearance 
• 6+ years of experience building and operating backend/platform systems in production 
• 3+ years building platforms that support AI/ML systems in production (e.g., evaluation pipelines, model/app runtime infrastructure, artifact/metadata registries, AI workflow orchestration, MLOps) 
• Experience operating LLM-enabled systems with production constraints (latency, cost, reliability), including monitoring quality/regressions and enforcing safe tool/data access 
• Strong proficiency in one or more backend languages (e.g., Go, Java, Python, C++) and modern infrastructure practices 
• Experience designing APIs, data models, and distributed systems with reliability and security best practices 
• Experience with workflow/event systems (queues, pub/sub, orchestration, idempotency, state machines) used to run multi-step AI-driven pipelines 
• Experience implementing observability for AI systems (metrics, tracing, logs) including quality/reliability signals beyond uptime (e.g., eval scores, rejection rates, cost/latency budgets) 
• Experience with production security controls: RBAC/ABAC, audit logs, secret