LLM Research Scientist (Summer Intern)

Key Skills

Aartificial intelligence, Algorithms, Analytical and Problem solving, Deep Learning, Effective communication skills, Large Language Models - LLMs, Linear Algebra, Machine learning techniques, Natural Language Processing (NLP), Python Programming, PyTorch, TensorFlow

Job Description

We are currently looking for individuals who can commit to an internship schedule of 2-5 days (16-40 hours) per week. This internship opportunity entails a minimum duration of 6 months, beginning from the present date. We advise prospective applicants to carefully assess their availability for this commitment before submitting their applications.

After the internship, there is an opportunity to transition from an intern to a full-time position.

Responsibilities

• Collaborate with senior research scientists to design and implement experiments for improving LLM performance.
• Conduct literature reviews and stay up-to-date with the latest advancements in natural language processing (NLP) and machine learning (ML).
• Develop and implement algorithms for training and fine-tuning large language models.
• Analyze and interpret experimental results to draw meaningful conclusions and insights.
• Participate in team meetings and contribute ideas to drive innovation in LLM research.
• Document research findings and contribute to academic publications and technical reports.
• Assist in the evaluation and benchmarking of LLM models using standard NLP datasets.

About you

[Minimum qualifications]

• Currently enrolled in a graduate program (Bachelor's, Master's, or Ph.D.) in computer science, artificial intelligence, machine learning, or a related field.
• Strong programming skills in languages such as Python, TensorFlow, or PyTorch.
• Solid understanding of machine learning concepts and algorithms.
• Excellent problem-solving skills and analytical thinking.
• Ability to work independently as well as collaboratively in a team environment.
• Good communication skills and the ability to present complex ideas effectively.

[Preferred qualifications]

• Previous research experience in natural language processing or machine learning.
• Familiarity with deep learning frameworks and libraries.
• Experience with large-scale data processing and distributed computing platforms.
• Strong mathematical background, including proficiency in linear algebra and probability theory.
• Publications in top-tier conferences or journals related to artificial intelligence or NLP.
• Demonstrated interest in advancing the state-of-the-art in language understanding and generation.