Principal Machine Learning Engineer

Microsoft Microsoft · Big Tech · New York, NY +4 · Software Engineering

Principal Machine Learning Engineer to work on data labeling and classification for large-scale multimodal Copilot data within Microsoft AI. The role involves prototyping and productionizing classification flows, operating prompted classifiers, and building secure data-labeling pipelines. Requires experience in data pipelines, data science, ML, and strong communication skills.

What you'd actually do

  1. Build evaluation loops (precision/recall, calibration, drift, human-in-the-loop) and publish dashboards/SLOs.
  2. Generalize machine learning (ML) solutions into repeatable frameworks.
  3. Operationalize prompted classifiers at scale (batch & streaming), including orchestration, autoscaling, monitoring, and cost guardrails.
  4. Conduct thorough review of data analysis and techniques used to summarize the process review and highlight areas that have been missed or need re-examining.
  5. Collaborate cross-functionally with DS, Security, and Platform to define schemas, access patterns, and governance.

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field
  • 6+ years technical engineering experience
  • coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python

Nice to have

  • 7+ years' experience writing production-quality Python or Java or Scala code
  • 5+ years' experience in distributed systems design and implementation of large scale data processing systems
  • 3+ years' experience building ML data pipelines using atleast one of AML, Promptflow, Langchain or LangGraph
  • Demonstrated interest in Responsible AI
  • Experience prompting, evaluating, and working with large language models

What the JD emphasized

  • production quality Python or Java or Scala code
  • large scale data processing systems
  • building ML data pipelines using atleast one of AML, Promptflow, Langchain or LangGraph
  • prompting, evaluating, and working with large language models

Other signals

  • production logs
  • prompted classifiers
  • data-labeling pipelines
  • large scale multi modal Copilot data