Senior AI Software Engineer

About this role

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.

Description

Reusable launch systems are the key to seamlessly connecting Earth and space. An instrumental ingredient to making reusable launch vehicles is the ability to move fast. That is our goal for Boltline by Stoke Space, our commercial software platform that helps advanced hardware manufacturing teams integrate, track, iterate, build, and test efficiently.

As a Senior AI Engineer on the Boltline team, you will design, build, and deploy intelligent systems that augment our platform's capabilities—from agentic workflows that automate complex manufacturing processes to RAG pipelines that surface critical institutional knowledge. You'll architect evaluation frameworks to ensure AI reliability, build developer tooling that empowers the team, and integrate LLMs into production systems that accelerate hardware development. The ideal candidate demonstrates deep expertise in applied AI/ML, a high degree of ownership, and curiosity—balancing fast execution with robust, production-grade engineering.

Responsibilities

• Design and implement agentic systems and workflows that automate complex, multi-step manufacturing and engineering processes

• Build and maintain RAG pipelines leveraging vector databases to enable intelligent search and retrieval

• Develop comprehensive evaluation frameworks (evals) to measure, monitor, and improve AI system performance, accuracy, and reliability

• Create developer tooling, SDKs, and internal platforms that accelerate AI adoption across the engineering organization

• Integrate LLM APIs and orchestration frameworks into production applications

• Design end-to-end features across the stack using React, TypeScript, GraphQL, and Postgres

• Lead technical design discussions and contribute to architectural decisions for AI-powered features