Applied Science PhD/M.Tech Intern

Job Description

Applied Sciences INTERN | Microsoft

The Tone:
This is an internship position at Microsoft, where individuals work to integrate cutting-edge research into real-world products and services. Interns contribute to the creation of intelligent solutions by applying data analysis, modeling, and machine learning techniques. This role is crucial for making a tangible impact on product quality and business outcomes, ensuring Microsoft products stay at the forefront of innovation.

The TL;DR
• Role: Internship
• Mission: Contribute to the creation of intelligent solutions through data analysis, modeling, and machine learning, ensuring products remain at the leading edge of innovation.
• Tech Stack: Python, PyTorch, TensorFlow

What You’ll Actually Do
• Design: Implement and evaluate machine learning and AI solutions for real-world product scenarios.
• Analyze: Improve the performance of advanced algorithms on large-scale datasets.
• Translate: Business and product challenges into machine learning research problems and experimental frameworks.
• Develop: Train, fine-tune, and evaluate machine learning models, including Large Language Models (LLMs) and Small Language Models (SLMs).
• Build: Scalable prototypes and AI-powered systems that can be deployed in production environments.

The Must-Haves
• Background: Doctorate Degree. Currently pursuing a PhD in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Electrical Engineering, Computer Engineering, Econometrics, or a related technical field, with at least one semester/quarter remaining following completion of the internship.
• Experience: Research experience in Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Generative AI, or related areas. Experience designing experiments, analyzing results, and applying statistical methods to solve research problems. Experience working with large datasets and building end-to-end machine learning pipelines.
• Skills: Strong programming in Python, experience with machine learning frameworks such as PyTorch or TensorFlow, ability to formulate hypotheses, conduct independent research, and communicate technical findings effectively.