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Eon is building next-generation cloud data protection, transforming cloud backups into accessible, searchable, AI-ready data assets by combining advanced data systems, storage technologies, and AI.
As a Senior Research Scientist in AI & Data Systems, 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 research in leading peer-reviewed venues, file patents, and translate research breakthroughs into production-ready product capabilities.
Key responsibilities include:
- Lead applied research at the intersection of AI, data systems, information retrieval, and large-scale storage
- Identify and investigate challenging technical problems related to making enterprise backup data more searchable, accessible, and useful for AI applications
- Design, implement, and evaluate research ideas through hands-on experimentation, prototyping, and benchmarking
- Develop novel approaches to query processing, data discovery, entity resolution, retrieval, and LLM-powered systems
- Collaborate closely with VP/Chief Architect and engineering/product teams to translate research findings into production-ready capabilities
- Publish research in leading peer-reviewed conferences and contribute to patent applications
- Build evaluation frameworks and benchmarks to measure quality, accuracy, and performance of AI systems operating on enterprise data
- Stay current with advances in AI and data systems, identify promising research directions, and turn them into practical innovations
Research areas include: query processing and query engines (query optimization over columnar and lakehouse formats, Apache Iceberg, Parquet, approximate query processing); storage systems (deduplication, compression, data layout optimization); data integration and discovery (entity resolution, schema matching, table joinability, LLMs as judges); information retrieval and RAG over enterprise data; LLM evaluation and benchmarking; and LLMs for software engineering (CI failure diagnosis, automated debugging, program repair).
REQUIREMENTS:
- PhD in Computer Science or closely related field (must be completed; candidates currently pursuing a PhD will not be considered)
- Deep, sustained research track record in at least one of the relevant research areas listed above
- Publications in strong peer-reviewed conferences such as VLDB, SIGMOD, ICDE, SIGIR, CIKM, FAST, OSDI, USENIX ATC, ACL, EMNLP, NeurIPS, ICML, ICLR, ICSE, or FSE (depending on research area)
- At least 5 years of industry research experience, including work deployed in production
- Hands-on experience with LLMs, including prompting, fine-tuning techniques (LoRA), model evaluation, and running batch inference on GPUs
- Strong Python skills and ability to independently design, implement, and execute end-to-end experiments
- Excellent written English and ability to communicate complex research findings clearly
NICE TO HAVE:
- Experience in storage, backup, data protection, or enterprise data management
- Research or engineering experience with query engines, lakehouse formats, or approximate query processing
- Experience with cloud platforms (AWS, Azure, GCP)
- Patents or track record of translating research into intellectual property
- Experience working with enterprise data sources (Microsoft 365, email, documents, databases)
