In this role, youll make an impact in the following ways:
Algorithmic Development and Mathematical Analysis.
Engage in the in-depth study and modification of algorithms that power Large Language Models.
Deep mathematical understanding of natural language processing techniques, model architectures, and optimization strategies is essential for refining or creating more efficient and effective models.
Technology Review and Benchmarking.
Keep abreast of the latest advancements in the Large Language Model landscape by continuously reviewing state-of-the-art technologies and frameworks.
Identify performance bottlenecks and operational limitations in existing systems, offering technological insights that can steer the development of next-generation models.
Conceptual Innovation and Prototyping.
Dedicate efforts to crafting novel methods and algorithms aimed at pushing the boundaries of what Large Language Models can accomplish.
Translate theoretical breakthroughs into testable prototypes, collaborating with engineering teams to evaluate their real-world applicability and scalability.
To be successful in this role, were seeking the following:
Ph.D. in Computer Science, Computational Linguistics, or a related field, with a specialization in natural language processing (NLP), machine learning, or a specific focus on Large Language Models.
Optional but highly desirable: Postdoctoral research experience or industry research experience in the area of Large Language Models or closely related fields.
Significant years of research experience in the field of natural language processing, machine learning, or data science, with at experience specifically focused on Large Language Models.
Experience in utilizing a range of machine learning frameworks and tools, such as TensorFlow, PyTorch, or similar, for the development and testing of language models.
Proven ability to collaborate effectively with multidisciplinary teams, including data scientists, engineers, and product managers, in bringing research concepts to prototype or production stages.
Familiarity with current ethical considerations related to Large Language Models, including issues of fairness, transparency, and bias.
