ML Postdoc Researcher - LLMs & Generative AI

Key Skills

Aartificial intelligence, Effective communication skills, Large Language Models - LLMs, Machine learning techniques, Natural Language Processing (NLP), Python Programming, PyTorch, TensorFlow

Job Description

Internship Details

Our Research PhD Internship is designed for candidates who have finished their classes and are working only on research work to complete their PhD. Candidates must be within 1 year of their graduation date from their PhD program. Candidates are expected to demonstrate both independence in defining their research strategy within a domain as well as an ability to apply innovative solutions to our products. Our internships are designed to be a minimum of 10 weeks with the opportunity to extend beyond the initially agreed term based on the company’s needs and the candidate’s desires.

This Opportunity

We are looking for machine learning experts who can utilize applied science and software development skills in building our Foundation Models that help us address some of the hardest problems towards our vision of Saving Lives with Data. You will work in an exciting and fast-paced environment, collaborating closely with multiple teams across the company. You will work as part of an organization that brings together talent from diverse backgrounds including software engineering, big data, machine learning and AI, clinical informatics, and medicine making our team an exciting place to work. We value and encourage diversity in the belief that our differences make us and our products better.

In this role, you will:

• 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.

Key Qualifications

• 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

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

This internship opportunity offers a unique chance to work on state-of-the-art language models and contribute to transformative research with the vision of Saving Lives with Data. You will be part of a dynamic team of researchers and engineers who are passionate about pushing the boundaries of machine learning and natural language understanding in the healthcare domain. Join us and make a significant impact on the future of healthcare and patient well-being.