MSAI organization plays a key role in Microsoft to build products that help boost Enterprise users' productivity.
We help users find relevant artifacts and get their daily jobs done in a timely and efficient manner.
MSAI is now responsible for delivering M365 Chat which is designed to help users find, summarize, and act upon their work data in an interactive pattern.
As a Senior Applied Scientist specializing in ranking recommendation ML models, you will play a pivotal role in shaping the future of our ranking systems (across different enterprise data like Emails, Documents, Calendar, Graph connectors, etc.).
You will collaborate closely with cross-functional teams to design, implement, and evaluate state-of-the-art algorithms that power our cross-entity ranking engine.
Leveraging your expertise in machine learning, you will drive innovation and continuously improve the performance and relevance of our retrieval stack.
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.
Responsibilities: Research and develop novel ML algorithms and techniques to enhance the ranking accuracy and effectiveness of retrieval systems, which in turn gets fed into LLMs for response generation.
Design and implement scalable and efficient algorithms for large-scale ranking tasks, considering factors such as user engagement, diversity, and fairness.
Conduct rigorous experiments and analysis to evaluate the performance of ranking models, identify areas for improvement, and iterate on existing solutions.
Collaborate with software engineers and data engineers to integrate ML models into production systems and optimize their performance.
Stay abreast of the latest advancements in machine learning, information retrieval, and recommendation systems, and contribute to the company's intellectual property through patents and publications.
Provide technical leadership and mentorship to junior team members, fostering a culture of innovation, collaboration, and continuous learning.
Qualifications: Required Qualifications: Ph.D. or Master's degree in Computer Science, Electrical Engineering, Statistics, or a related field with a focus on machine learning, information retrieval, or recommender systems.
Experience in developing and deploying ranking recommendation ML models and LLMs in real-world applications.
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