About the role
Job description:
Are you a real machine learning enthusiast? Does the magic of the data world fascinate you? Here at the Copilot Search & Agents team, we have been working on machine learning much before it became cool to do so. Here we are solving real-world problems to empower millions of people around the globe.
Microsoft Search powers delightful and relevant experiences for enterprise users to cater to their overall information need. As part of this team, you would get a chance to work on the core search stack powering all Copilot Search scenarios.
We’re looking for an Applied Scientist II with deep expertise in applied AI and a solid track record of delivering products built using ML models at scale. The candidate should have hands-on experience with building machine learning models over large-scale data. This is a unique opportunity to build the next-generation search stack in Microsoft.
You’ll join a fast-paced, collaborative environment that brings together talent from across Microsoft—including Microsoft Research—to push the boundaries of what’s possible with state-of-the-art SLMs, deep neural nets, embedding models, as well as classical Gradient Boosted Tree (GBT) models.
Responsibilities:
- Advance the science: Design and run experiments, define and validate metrics, and develop ML pipelines and models in areas like encoder-decoder models and cross-encoder models. You would also be working with language models to understand user intent and developing a semantic search stack optimizing for the various search intents. You will be working with large-scale data and derive insights out of it while championing privacy and compliance.
- Drive product innovation: Partner with Engineering, PM, and Design to translate product vision and industry trends into scalable, reliable, and agile architecture.
- Collaborate across Microsoft: Work closely with Microsoft Research, Azure, AI platform teams, and product groups to bring cutting-edge AI into real-world applications.
- Champion customer impact: Engage with customers and internal teams to deeply understand pain points and drive improvements that matter.
- Define success: Measure success metrics that reflect platform and product growth.
- Stay ahead: Keep up with the latest research and foster a thriving applied science culture. Publishing is encouraged, though not required.
Qualifications:
- Required/Minimum 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 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.
Preferred Qualifications:
- Bachelor's Degree in Statistics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ years related experience (e.g., statistics, predictive analytics, research).
- OR Master's Degree in Statistics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research).
- OR Doctorate in Statistics, Computer Science, Electrical or Computer Engineering, or related field and related experience (e.g., statistics, predictive analytics, research).
- OR equivalent experience.
- 3+ years of Python coding skill.