Post Master

We are looking for a computer scientist (US citizen) to join our team in researching the performance and
efficacy of machine learning and artificial intelligence (large language models - LLMs) across a variety of
both research-oriented and public-facing software applications using standardized science-based
metrics. The initial focus is on developing LLM-based outputs, and comparing their performance relative
to manually developed outputs, including drafting annotated bibliographies and literature reviews,
writing code, web applications that incorporate LLMs to enhance capabilities, and LLM-based web
applications.
The ideal candidate will have a strong background in integrating LLM APIs, React, and front-end
programming, and will be responsible for transforming models into usable APIs or integrated tools for
production. They will also be responsible for monitoring, troubleshooting and enhancing model
efficiency and scalability. The candidate should also be well versed in handling data preprocessing, and
analysis for model training. The candidate should be aware of various prompt engineering techniques,
implementing intelligent prompt caching (e.g., Redis), understanding of vector stores (e.g., Pinecone),
and efficient token management. The candidate should have knowledge of implementing AI security
protocols, including guardrails and techniques to prevent prompt injection. The candidate should also
have a working knowledge of RAG (Retrieval Augmented Generation). Additional research tasks may be
assigned based on candidate’s skillset and priorities.