Staff Applied Scientist - Large Language Models
: Job Details :


Staff Applied Scientist - Large Language Models

Strativ Group

Location: all cities,CA, USA

Date: 2024-11-06T15:31:36Z

Job Description:

Staff Applied Scientist - Large Language Models

Strativ Group are currently partnered with a world leading AI startup (founder sold his previous startup to Microsoft for $800mil) who have significant growth plans within their AI team, specifically relating to their work in Generative AI & LLMs.

They are hiring for a Staff Applied Scientist to lead their AI & ML R&D, focusing both on ML/LLM Engineering and Research,.

We are specifically looking for individuals with PhDs in NLP, Machine Learning, NLP or Computational Linguistics, with a proven publication history. Commercial research in Large Language Models / Generative AI is also essential and the ideal candidate will have experience in a Big-Tech / FAANG environment, or at a recognized leader in Generative AI.

You will be responsible for:

  • ML/LLM Engineering
  • Research and Development - conducting cutting-edge research in Generative AI and Foundation Models, focused on advancing the capabilities of language understanding, generation, and representation.
  • Innovation, Publishing & Thought Leadership - driving innovation in GenAI & Foundation Models by staying current with the latest advancements and contributing to the development of novel algorithms and models.
  • Model Evaluation and Improvement - evaluating the performance of existing Models and propose enhancements to address limitations and optimize for specific applications.
  • Collaboration & Mentorship - collaborating with internal teams to integrate Models into various products and solutions, and providing mentorship and guidance to junior researchers, fostering a collaborative and innovative research culture.

Qualifications:

  • PhD educated - proven publication history and research across NLP, RL, Generative Modelling or LLMs
  • Strong commercial experience as a Research Scientist focused on Large Language Models, ideally at scale
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