Senior Machine Learning Engineer

Microsoft•Mountain View, CA

2d•$119,800 - $234,700

About The Position

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.

Requirements

Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ 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 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate 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 equivalent experience.
Proven experience in NLP, including experience with modern transformer architectures for tasks such as classification, encoding, summarization, and semantic search
Experience with text embedding models, vector databases, and retrieval-augmented generation (RAG) patterns
Familiarity with distributed training, model optimization, and serving ML models at scale
Experience with search ranking, relevance modeling, or information retrieval systems
Proficiency in Python and ML frameworks such as PyTorch, Hugging Face Transformers, or similar
Experience working with data platforms (e.g., Spark, Databricks, Azure ML) and building end-to-end ML pipelines from data ingestion through model deployment

Nice To Haves

Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR equivalent experience.

Responsibilities

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