Staff Engineer (Founding)

About the Role

A fast-growing, seed-stage AI enterprise SaaS company based in Munich is looking for a Founding Staff Engineer to serve as the technical backbone of a small, high-velocity engineering team. This is a deeply hands-on role — you'll own the highest-leverage systems across multiple products, set architectural direction, and raise the bar on quality, reliability, and AI capabilities. The company automates inside sales workflows (quoting, order processing, voice agents) for distributors and manufacturers, with enterprise-grade ERP/CRM integrations and a growing roster of customers across Europe.

This is an early, high-impact seat at a well-backed startup (Y Combinator) with a strong technical team and a clear path to scale. You'll work in-person in Munich with a team that operates with intensity and purpose.

What You'll Do

• Serve as the technical backbone across multiple products and development streams, owning the most complex, highest-leverage engineering work.

• Define and evolve architecture decisions that prevent long-term dead-ends, spanning core systems, shared frameworks, and critical infrastructure.

• Spend the majority of your time writing and shipping production code — leading by example.

• Eliminate technical debt through targeted refactoring, improving reliability and raising engineering standards across the board.

• Mentor engineers via code review, pairing, and direct feedback to reduce hidden risk and multiply team output.

• Contribute to the architecture and implementation of AI capabilities, including LLM pipelines and data infrastructure.

What We're Looking For

Must-haves:

• 8+ years of software engineering experience with a proven track record of solving genuinely hard technical problems in production.

• Deep full-stack expertise: strong backend, API, and infrastructure skills; comfortable on the frontend when needed (Python, React, TypeScript).

• Proven experience designing and evolving systems across multiple products and teams, including long-term architectural strategy.

• Experience contributing to AI capabilities — design and implementation of LLM pipelines and data infrastructure.

• Cloud and DevOps fluency: containerization (Docker), cloud platforms (Azure / AWS / GCP), and infrastructure as code (Terraform).