Senior Research Scientist, AI & Data Systems

About the role

Eon builds the next generation of cloud data protection, turning cloud backups into searchable, AI-ready data assets. As a Senior Research Scientist at Eon Research, you will conduct applied research at the intersection of AI, data systems and large-scale storage, working closely with Assaf Natanzon, VP and Chief Architect. You will explore challenging technical problems, publish in leading peer-reviewed venues, file patents and turn research into production-ready product capabilities.

Key responsibilities

• Lead applied research on query processing, storage systems, data integration, information retrieval, RAG, LLM evaluation and LLMs for software engineering.
• Identify and investigate technical problems that make enterprise backup data more searchable, accessible and useful for AI applications.
• Design, implement and evaluate research ideas through experimentation, prototyping and benchmarking.
• Develop novel approaches to query processing, data discovery, entity resolution, retrieval and LLM-powered systems.
• Collaborate with the engineering and product teams to translate research into production-ready capabilities.
• Publish research in top conferences and contribute to patent applications.
• Build evaluation frameworks and benchmarks for AI systems on enterprise data.

Required profile

• Completed PhD in Computer Science or a closely related field; PhD candidates are not considered.
• Deep, sustained research track record in at least one of the listed areas.
• Publications in strong peer-reviewed venues such as VLDB, SIGMOD, ICDE, SIGIR, CIKM, OSDI, USENIX ATC, ACL, EMNLP, NeurIPS, ICML, ICLR, ICSE or FSE.
• At least 5 years of industry research experience, including deployed work.
• Excellent written English.

Required skills

• Hands-on experience with LLMs: prompting, fine-tuning (e.g. LoRA), model evaluation and batch inference on GPUs.
• Strong Python and independent end-to-end experiment design and implementation.
• Nice to have: storage, backup or data protection experience; query engines or lakehouse formats; AWS, Azure or GCP; patents; enterprise data sources such as Microsoft 365, email and databases.