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AI/NLP Research Engineer

GigaSpaces is developing a pioneering eRAG (Enterprise Retrieval Augmented Generation), a technology that aims to enable LLMs to query structured enterprise data with human-level accuracy. GigaSpaces is on a mission to open up a whole new world of opportunities for enterprises and large organizations in the way they utilize AI solutions over their internal business data.

We are seeking a highly skilled AI/NLP Research Engineer to join our specialized team of deep research efforts in natural language interaction with structured data. The ideal candidate will have a strong academic background in AI and NLP, with a focus on deep learning methods and their theoretical underpinnings. This individual will play a crucial role in driving our research agenda forward with the goal to become a leader in this area focusing on results accuracy and real life use cases.

Our offices are located in Herzliya, and the employment can be flexible – either full time or part, company employee or external consultant.

Responsibilities:

  • Lead and conduct advanced research in natural language interaction with structured data, with a focus on choosing and applying NLP Algorithms and models as well as innovate and improve on them to suppress existing best of breed.
  • Collaborate closely with cross-functional teams to understand product requirements and translate them into research objectives and experiments.
  • Stay current with the latest advancements in AI and NLP research, and apply findings to enhance our technology and products.
  • Provide guidance on research methodologies, best practices, and technical skills development.

Requirements:

  • M.A/Ph.D. or equivalent in Mathematics, Computer Science, Artificial Intelligence, Machine Learning, or a related field.
  • 2+ year proven track record of applying NLP techniques to real-world problems, with hands-on experience in developing and deploying AI solutions.
  • Experience in NLP research, with a focus on natural language interaction with structured data.
  • Theoretical background in deep learning, particularly in the area of large language modeling (LLM).
  • Background in vector databases, MLOps, CI/CD pipeline development, model quantization and LLM deployment – advantage
  • Experience in finetuning and evaluating LLMs – advantage
  • Demonstrated expertise in Python and relevant machine learning tools.
  • Familiarity with statistical modeling/analysis techniques. Knowledge in R, Matlab, or similar tools for statistical analysis is an advantage.
  • Excellent communication skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders.
  • Creative thinker with a passion for pushing the boundaries of AI research and innovation.
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