Microsoft Research Intern - Memory & Orchestration Large Language Models 

United States, Washington 

410286924

• Currently enrolled in a PhD program in Computer Science, Machine Learning, Artificial Intelligence, or a related STEM field.

Other Requirements

• Research Interns are expected to be physically located in their manager’s Microsoft worksite location for the duration of their internship.
• In addition to the qualifications below, you’ll need to submit a minimum of two reference letters for this position as well as a cover letter and any relevant work or research samples. After you submit your application, a request for letters may be sent to your list of references on your behalf. Note that reference letters cannot be requested until after you have submitted your application, and furthermore, that they might not be automatically requested for all candidates. You may wish to alert your letter writers in advance, so they will be ready to submit your letter.

• Experience with large language models and multimodal models, including training and fine-tuning.
• Familiarity with embeddings, including graph embeddings and multimodal embeddings.
• Familiarity with the architecture of generative models, such as variational autoencoders and diffusion models.
• Experience building and deploying retrieval augmented generation systems.
• Experience working in interdisciplinary teams, with a focus on AI research and development.
• Strong programming skills in Python and familiarity with AI/ML frameworks such as PyTorch or TensorFlow.
• Previous experience publishing academic research in top-tier conferences or journals.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

• Conducting hands-on research into systems for memory and orchestration of LLMs and multimodal models.
• Investigating new embedding techniques, including graph embeddings and multimodal embeddings.
• Developing advanced retrieval augmented generation systems to enhance LLM capabilities.
• Specializing in context-specific fine-tuning for creating adaptable AI systems.
• Collaborating with interdisciplinary teams of researchers and engineers on challenging and impactful projects.
• Presenting research findings and participating in research discussions.