Machine Learning Software Engineer

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

Machine Learning Engineer to build AI Insights, a Copilot analytics product. This role involves designing and implementing AI-driven trend detection, cohort analysis, and drill-down workflows that connect metrics to real user conversations using large-scale multimodal Copilot data. Responsibilities include building data pipelines, implementing ML models, developing secure workflows, and enabling drill-down capabilities linking quantitative metrics to qualitative evidence.

What you'd actually do

  1. Build scalable data pipelines for telemetry ingestion, anomaly detection, and cohort segmentation.
  2. Implement ML-driven insights (prompted classifiers, anomaly detection) and integrate them into dashboards and APIs.
  3. Develop secure, compliant workflows for handling production logs and conversation data.
  4. Enable drill-down capabilities linking quantitative metrics to qualitative evidence for actionable context.
  5. Collaborate with PMs and DS to refine hypotheses and deliver intuitive, high-performance interfaces.

Skills

Required

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

Nice to have

  • Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • Proven experience leading small engineering and machine learning teams
  • collaborating effectively with cross-functional stakeholders including product managers, UX designers, and security specialists
  • Demonstrated interest in Responsible AI

What the JD emphasized

  • production logs and conversation data
  • quantitative metrics to qualitative evidence
  • prototype and productionize ML models

Other signals

  • AI-driven trend detection
  • ML-driven insights
  • prototype and productionize ML models