Senior Machine Learning Engineer

As Microsoft continues to push the boundaries of AI, we are on the lookout for passionate individuals to work with us on the most interesting and challenging AI questions of our time. Our vision is bold and broad — to build systems that have true artificial intelligence across agents, applications, services, and infrastructure. It's also inclusive: we aim to make AI accessible to all — consumers, businesses, developers — so that everyone can realize its benefits.

Microsoft AI (MAI) is seeking a Senior Machine Learning Engineer to join the Growth Intelligence team and contribute to the evolution of Copilot, our personal AI assistant.

In this role, you will design and build models and ML pipelines for user and conversation understanding — helping Copilot better interpret user intent, extract meaning from conversations, and deliver more relevant, personalized experiences. You will work across the full model lifecycle, from data preparation and training to evaluation and production deployment.

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.

• Build and Deploy Models: Design, train, evaluate, and deploy machine learning models for natural language understanding tasks including intent detection, topic classification, conversation summarization, and user personas.
• Design ML Pipelines: Architect scalable, production-grade training and inference pipelines using Spark, Databricks, Azure ML and modern ML frameworks.
• NLP and Representation Learning: Develop and fine-tune transformer-based models and text encoders; build and maintain embedding pipelines and vector databases for semantic search and retrieval.
• Experimentation and Evaluation: Drive rigorous offline and online experimentation to measure model quality, iterate on architectures, and improve key product metrics.
• Collaborate Across Teams: Partner with data engineers, data scientists, and product teams to translate research insights into shipped features and align model outputs with product goals.
• Show Ownership Mindset: Proactively monitor model performance in production, diagnose regressions, and address scalability and reliability challenges before they become bottlenecks.
• Contribute Strategically: Identify opportunities to improve model architectures, training methodologies, and evaluation frameworks; mentor others on ML best practices.

Required/minimum qualifications: 

Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $158,400 - $258,000 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

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.