About the job
About Zaimler
At Zaimler, we believe that AI agents must possess the capability to interpret data deeply and meaningfully. Currently, enterprise data is scattered across numerous systems devoid of a unified context, leading to a significant gap in the efficacy of enterprise AI solutions. As we transition from copilots to fully autonomous agents, we are pioneering a new layer of infrastructure to address these challenges.
Zaimler serves as the contextual infrastructure for this agentic era, providing a platform that autonomously uncovers domain knowledge, establishes relationships, and equips AI agents with the semantic understanding necessary for precise and scalable operations. Picture knowledge graphs that facilitate real-time inference tailored for systems that require reasoning rather than mere data retrieval.
Founded by industry veterans Biswajit Das (former VP of Engineering at Truera) and Sofus Macskassy (former Director of Engineering at LinkedIn), who developed one of the largest knowledge graphs in operation today, we are a small yet highly experienced team at the seed stage, collaborating with leading enterprises in sectors such as insurance, travel, and technology. If you're eager to contribute to the foundational infrastructure that will drive the next decade of AI, we would love to connect.
About the Internship
We are seeking motivated interns to join our team for either part-time engagements throughout the year or full-time during the summer. The ideal candidate will have a passion for and some experience in areas such as Knowledge Extraction, Natural Language Understanding, Unsupervised Learning, Information Retrieval, and Fine-tuning LLMs. In this role, you will be instrumental in developing and training models, pipelines, and methodologies that underpin our semantic graph systems. This internship offers the opportunity to work with large-scale, real-world data with the aim of structuring it for discoverability and comprehension by end-users. You will engage with advanced models and techniques encompassing LLMs, machine learning, natural language processing, and semantic technologies.