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Founded in 2023 in Berlin, Luup is an AI startup dedicated to shaping the future of corporate sustainability through environmentally conscious and responsible corporate governance.
Luup supports medium-sized companies in the implementation of sustainability management. With our AI agents, we not only help companies define their sustainability goals, but also jointly develop tailor-made strategies and measures that help the EU Green Deal become a success. The current and target status of the companies is recorded in reports that are created in our software.
With the potential to impact over 45,000 companies, we’re at the forefront of corporate sustainability and innovation.
Every day, we’re working with small and medium sized businesses that have the biggest impact on our environment, helping them navigate the complex world of new regulations (CSRD).
We have a live product on the market for six months, have already 20+ paying customers and successfully completed our financing round. Now, we are looking for help to build Luup v2.
At Luup, we ❤️ high-quality software that is fast, safe, and well-designed. We employ modern technologies like SolidJS + Typescript for our frontend, Python for AI, Golang and k8s for DevOps, or Rust 🦀 as the language of choice for backend services towards our goal of technical excellence.
As a Data Scientist / Data Engineer, you will lead the development of our AI pipeline, focusing on retrieval-augmented generation (RAG) while utilizing various foundational models and vector stores.
Your work will involve fine-tuning large language models (LLMs) for specific use cases, and establishing robust observability systems. You will develop and optimize data pipelines for data collection, pre-processing, and seamless integration with large-scale machine learning models.
You will have a lot of freedom and responsibility to develop iterative feedback loops to incorporate real-time data and enhance model accuracy and relevance.
Collaborating closely with product managers and engineers, you will define and prioritize AI features and capabilities to meet the evolving needs of the organization.