Applied Scientist II

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We are a world-class R&D team of passionate and talented scientists and engineers who aspire to solve tough problems and turn innovative ideas into high-quality products and services. We help hundreds of millions of users find what they want, and advertisers gain the right audience, thereby directly impacting our business as a Marketplace. We are looking for an Applied Scientist II for our team.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Required Qualifications:

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research)
  
  • OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
  • OR equivalent experience.
• 2+ year(s) of Experience in machine learning and deep learning technologies. In particular, hands-on experiences with deep learning models (DNN, Attention, CNN, RNN) and frameworks (TensorFlow, PyTorch, Keras, etc.).
• 1+ years of experience with Large Language Models (LLMs).

Preferred Qualifications:

• Experience in delivering, scaling, and maintaining highly successful and innovative machine learning products with your fingerprints all over them.
• Experience in algorithm and analytical background and understanding on how to apply advanced knowledge to solve real problems
• Ability to work independently in a team to deliver innovative solutions solving challenging business/technical problems from high level vision and architecture, down to quality design and implementation.
• Experience in parallel or distributed processing, high performance computing, stream computing and SCOPE is a plus.
• Demonstrated experience in working with LLMs, such as GPT, BERT, or similar models, including knowledge of their strengths, limitations, and capabilities.
• In-depth knowledge of natural language processing (NLP) techniques and concepts, including tokenization, semantic analysis, and text generation.
• Self-motivated and self-directed and be able to work constructively with a wide variety of people, team and changing business priorities.

Applied Sciences IC3 - The typical base pay range for this role across the U.S. is USD $100,600 - $199,000 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $131,400 - $215,400 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay

Microsoft will accept applications for the role until June 17, 2025.

#MicrosoftAI

• Building and maintaining production machine learning models for ad retrieval, quality prediction and ad ranking.
• Finding insights and forming hypothesis on web-scale data with various machine learning, feature engineering, statistical, and data mining techniques: e.g. regression, classification, NLP, optimization, p-values analysis.
• Designing experiments, understanding the resulting data, and producing actionable, trustworthy conclusions from them.
• Crafting and Optimizing Prompts for Effective Large Language Models (LLMs) Performance: Design, test, and refine prompts to elicit accurate, relevant, and useful responses from LLMs. This involves understanding the nuances of how the model interprets different inputs, experimenting with various prompt formulations, and iterating based on performance metrics and user feedback.
• Wrangling large amounts of data (think petabytes) using various tools, including open-source ones and your own.
• Taking complex problems and the associated data and giving the answers in a concise form to assist senior executives in making key business decisions.