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# Founding Senior Software Engineer (Netherlands)

- **Company:** Clipbook
- **Location:** Amsterdam, Netherlands
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, BigQuery, Cloud Computing, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Distributed Systems, Fault Tolerance, PostgreSQL, Machine Learning, Inference Optimization, TypeScript, Web Services, Retrieval-Augmented Generation, Large Language Models, Apache Spark, Backend, Containerization, Stripe, Kubernetes, Apache Flink, Apache Kafka, Build Tools, Data Pipelines, Docker
- **Published:** October 12, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=2f7082dc683d7161

## About the Role

* 3+ years building and scaling production systems. Strong across backend fundamentals (services, data models, distributed systems), with deeper expertise in one or more areas - and a desire to keep expanding your breadth over time.
 * Genuine excitement to build quickly & ship fast. We care about getting things into users' hands and iterating from there.
 * Experience with cloud infrastructure, containerization, and CI/CD. Familiarity with AWS/GCP, containerization (Docker/K8s), and CI/CD pipelines.
 * Excellent communication skills

Technical Areas Where Depth Matters (one or more)

 * Data Engineering: Spark, Kafka, Flink, BigQuery, streaming pipelines, ETL at scale.
 * Distributed Systems: High-volume scaling, fault tolerance, eventual consistency.
 * AI/ML: LLMs in production, RAG, embeddings, fine-tuning, inference optimization.
 * Backend: Python, TypeScript, PostgreSQL, high-concurrency systems.

## Description

You'd be a key contributor at Clipbook - joining a small but mighty engineering team with engineers from Meta, Stripe, and AWS. You'll have the opportunity to develop new features and capabilities, and what you build will ship to 200+ customers quickly., * Architect & build core systems. Drive key decisions across our stack and own feature development end-to-end.

 * Integrate AI into real-world workflows. Put LLMs and ML models into production for real workflows: RAG pipelines, embeddings, prompt execution, agentic systems, fine-tuned models.
 * Design systems that scale. Build systems that will hold up as we grow 10x, while being practical about what to invest in now vs. what can wait.
 * Develop performant APIs and services. Create robust interfaces and internal services that power our product end-to-end, ensuring reliability, security, and a seamless experience for customers.