ML PhD Intern – Llms & Generative AI

ML PhD Intern – Llms & Generative AI

Requirements

• Currently pursuing a Ph.D. in Computer Science, Electrical Engineering, or a related field, with a focus on machine learning, natural language processing (NLP), Large Language Models (LLMs), multi-modal foundation models, and generative AI
• Strong theoretical and practical background in NLP including experience with state-of-the-art architectures
• Proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow, etc.) and libraries commonly used in NLP and Generative AI
• Solid programming skills in Python and the ability to write clean, efficient, and well-documented code
• Excellent problem-solving and troubleshooting abilities, along with a strong analytical mindset and persistence in resolving problems
• Strong communication skills and the ability to work effectively in a collaborative research environment

Responsibilities

• Collaborate with researchers and engineers to design, develop, and refine large language models and generative models for various applications
• Utilize your expertise in machine learning and natural language processing to develop novel algorithms and methodologies for generative modeling tasks
• Implement, train, and fine-tune LLM and GPT-like models on large-scale datasets to ensure optimal performance and accuracy
• Stay up to date with the latest research advancements and techniques in the field of language modeling, generative modeling, and machine learning
• Deliver the next generation of innovation in trustworthy healthcare

Preferred Qualifications

• Experience with distributed parallel training, large-scale multi-modal foundation and generative models
• Familiarity with parameter-efficient tuning techniques, Reinforcement Learning from Human Feedback (RLHF), and prompt engineering techniques
• Familiarity with training multi-modal foundation models
• Familiarity with cloud-based infrastructure and experience deploying large-scale machine learning models in production environments
• A track record of publications and contributions to the machine learning and natural language processing communities