Senior Machine Learning Engineer

Microsoft Microsoft · Big Tech · Mountain View, CA +4 · Applied Sciences

Senior Machine Learning Engineer for Microsoft's Growth Intelligence team, focusing on Copilot. The role involves designing, building, and deploying ML models and pipelines for user and conversation understanding, including intent detection, topic classification, summarization, and persona generation. Key responsibilities include NLP, representation learning with transformer models, embedding pipelines, vector databases, RAG, and rigorous experimentation for production deployment and improvement.

What you'd actually do

  1. Design, train, evaluate, and deploy machine learning models for natural language understanding tasks including intent detection, topic classification, conversation summarization, and user personas.
  2. Architect scalable, production-grade training and inference pipelines using Spark, Databricks, Azure ML and modern ML frameworks.
  3. Develop and fine-tune transformer-based models and text encoders; build and maintain embedding pipelines and vector databases for semantic search and retrieval.
  4. Drive rigorous offline and online experimentation to measure model quality, iterate on architectures, and improve key product metrics.
  5. Partner with data engineers, data scientists, and product teams to translate research insights into shipped features and align model outputs with product goals.

Skills

Required

  • Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience OR Master's Degree AND 3+ years related experience OR Doctorate AND 1+ year(s) related experience OR equivalent experience.
  • Python
  • ML frameworks such as PyTorch, Hugging Face Transformers, or similar

Nice to have

  • Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience OR Doctorate AND 3+ years related experience 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
  • Experience working with data platforms (e.g., Spark, Databricks, Azure ML) and building end-to-end ML pipelines from data ingestion through model deployment

What the JD emphasized

  • production deployment
  • production
  • scalability and reliability challenges

Other signals

  • Copilot
  • user and conversation understanding
  • intent detection
  • topic classification
  • conversation summarization
  • user personas
  • transformer-based models
  • text encoders
  • embedding pipelines
  • vector databases
  • semantic search
  • retrieval-augmented generation (RAG)
  • search ranking
  • relevance modeling
  • information retrieval systems