DevJobs

Senior Research Scientist, AI & Data Systems

Overview
Skills
  • Python Python
  • Microsoft 365
  • AWS AWS
  • Azure Azure
  • GCP GCP
  • Model evaluation
  • LoRA
  • LLMs
  • Information Retrieval
  • GPU
  • Query Engines
  • Query Processing
  • RAG
  • Batch inference
  • Program repair
  • Schema matching
  • Apache Iceberg
  • Table joinability
  • Parquet
  • Entity resolution
  • Deduplication
  • Data layout optimization
  • Data discovery
  • Compression
  • CI failure diagnosis
  • Automated debugging
About Eon

Eon is building the next generation of cloud data protection, transforming cloud backups into accessible, searchable, AI-ready data assets. We combine advanced data systems, storage technologies, and AI to help enterprises unlock the value of their backed-up data.

The Role

As a Senior Research Scientist, AI & Data Systems 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 research in leading peer-reviewed venues, file patents, and translate research breakthroughs into production-ready product capabilities.

This role is ideal for a researcher with a strong publication record, deep expertise in a relevant research area, and experience bringing research into real-world products.

Research Areas

  • Query Processing and Query Engines: Query optimization and execution over columnar and lakehouse formats, including Apache Iceberg and Parquet, as well as approximate query processing using sketches and synopses.
  • Storage Systems: Deduplication, compression, and data layout optimization for large-scale storage.
  • Data Integration and Discovery: Entity resolution, schema matching, table joinability, and data discovery, including the use of LLMs as judges.
  • Information Retrieval and RAG: Retrieval-augmented generation over enterprise data, including emails, documents, and databases.
  • LLM Evaluation and Benchmarking: Evaluation frameworks and benchmarks for LLM agents working with enterprise data.
  • LLMs for Software Engineering: CI failure diagnosis, automated debugging, and program repair.

Requirements

  • PhD in Computer Science or a closely related field. The degree must be completed; candidates currently pursuing a PhD will not be considered.
  • A deep, sustained research track record in at least one of the relevant 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 the research area.
  • At least 5 years of industry research experience, including work that has been deployed in production.
  • Hands-on experience with LLMs, including prompting, fine-tuning techniques such as LoRA, model evaluation, and running batch inference on GPUs.
  • Strong Python skills and the ability to independently design, implement, and execute end-to-end experiments.
  • Excellent written English and the 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 such as AWS, Azure, or GCP.
  • Patents or a track record of translating research into intellectual property.
  • Experience working with enterprise data sources, including Microsoft 365, email, documents, and databases.

What You'll Do

  • 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 Assaf Natanzon, VP and Chief Architect, and Eon's engineering and 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 the 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 for Eon's platform
Eon