Senior AI Infrastructure Engineer

At Stoke, we believe a thriving space economy will enable a vibrant, sustainable, and equitable future here on Earth. That is why we’re building Nova, our fully and rapidly reusable launch vehicle. Designed for daily flight, Nova tackles the core challenges of space transportation by reducing cost, increasing availability, and improving reliability. By radically lowering launch costs and increasing flight cadence, we’re helping create a truly scalable space industry.

Our team is mission-driven, collaborative, and empowered to take ownership of their work. If you want to work alongside some of the most dedicated and talented people on Earth, we’d love to have you join us.

Reusable launch systems are the key to seamlessly connecting Earth and space. Just as our rocket systems are designed to be reliable, automated, and intelligent, the internal tools that power our engineering and business operations must embody those same principles to help our teams move faster, work smarter, and stay focused on the mission.

We are looking for a Senior AI Infrastructure Engineer to design, build, and deploy AI-powered tooling across Stoke’s internal systems. As part of our IT Internal Tooling team, you will identify high-leverage opportunities to apply AI - including large language models, agentic systems, retrieval pipelines, and intelligent automation - and turn them into production‑grade tools that meaningfully accelerate the people building rockets.

This role requires deep technical expertise in modern AI engineering, software development, and systems integration, combined with a strong product instinct for what to build, what not to build, and how to ship AI tooling that actually works. You will work closely with engineers, operators, and business stakeholders across Stoke to understand their workflows, identify where AI can remove friction or unlock new capabilities, and design and implement robust solutions end‑to‑end.

This is a high‑impact role where your work directly shapes how our company operates and how quickly our teams can deliver.

You must be ready to stay focused, move quickly, self‑direct, and learn on the fly.

• Design, develop, and deploy AI‑powered internal tools end‑to‑end – from initial use‑case discovery and prototyping through production deployment, monitoring, and iteration
• Build LLM‑powered applications, agents, retrieval‑augmented generation (RAG) systems, and intelligent workflow automations that integrate with Stoke’s internal systems and data sources
• Partner with stakeholders across engineering disciplines, manufacturing, supply chain, finance, and other teams to identify high‑leverage opportunities for AI tooling and translate them into clear technical requirements
• Evaluate and select foundation models, frameworks, and platforms; build robust prompt, evaluation, and guardrail systems; and make pragmatic build‑versus‑buy decisions
• Develop and maintain the AI engineering stack used by the team, including model gateways, vector stores, evaluation harnesses, observability, and deployment pipelines
• Implement rigorous evaluation, testing, and monitoring practices for AI systems, including offline evals, online metrics, regression tracking, and human‑in‑the‑loop review
• Design and implement integrations with internal systems and data including; telemetry data, systems requirements, document stores, code repositories, and business applications – using APIs, webhooks, and event‑driven patterns
• Apply strong security, and compliance practices to AI tooling, including data handling, access controls, prompt injection defenses, and ITAR/export‑control considerations
• Operate AI services in production, including capacity planning, cost optimization, latency tuning, and incident response
• Produce clear technical documentation, runbooks, and architectural decision records; mentor team members on applied AI best practices and help raise the bar across the organization

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related technical field, or equivalent practical experience
• Combined 5–8 years of experience in software engineering, applied…