> Markdown version of [/jobs/ext/4501745-founding-engineer-full-stack-ai](https://www.wearedevelopers.com/jobs/ext/4501745-founding-engineer-full-stack-ai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Founding Engineer (Full-Stack/AI) - **Company:** Clera - **Location:** München, Germany - **Experience:** Experienced - **Salary:** €60,000.0 - €90,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Python (Programming Language), Systems Integration, Large Language Models, Backend - **Published:** October 31, 2026 - **Apply:** https://www.careerjet.de/jobad/defb1a808552236e21967faefde8ed2f3b ## About the Role Typically 2 to 3 years of professional experience, or equivalent relevant projects and demonstrated ability. Strong full-stack engineering skills, including substantial backend product development and experience working across cloud environments. Fluency in Python; familiarity with Go is a plus. Practical experience building or integrating production AI/ML or LLM systems, including benchmarking, fine-tuning, and experimentation. Evidence of building robust production systems, with comfort working at scale, automating systems, and improving complex codebases. Strong computer science fundamentals, adaptability, self-direction, and a high sense of ownership. Startup or entrepreneurial experience is valued. Freight, logistics, or supply-chain experience is a plus. ## Description Join an early-stage freight and logistics technology team as a hands-on founding engineer. You will shape the core product and technical direction by combining full-stack product engineering with practical AI experimentation. What You'll Do Design, build, and maintain critical product components, taking ownership from architecture through delivery. Develop reliable full-stack features that improve user efficiency. Evaluate, benchmark, and fine-tune AI capabilities for accuracy and domain relevance. Experiment with models, frameworks, and agent techniques in production, then iterate using data and user feedback. Improve complex legacy code and build scalable infrastructure and automation for data and AI workloads. Work closely with the founding team and colleagues as a hands-on, high-ownership contributor.