Senior Software Engineer - Browser Platform

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Software Engineering

Senior Software Engineer to join the on-device ML team for the Edge browser. The role involves delivering on-device ML capabilities via web APIs, integrating models and runtimes into Edge, developing evaluation pipelines and datasets, and contributing to the Chromium project. The focus is on shipping AI-powered features to end-users.

What you'd actually do

  1. Collaborate with teams across many parts of Microsoft, including with researchers to evaluate and optimize cutting-edge on-device models.
  2. Develop evaluation pipelines and datasets to ensure models meet developers’ needs.
  3. Integrate the models and runtimes into Edge and collaborate to improve the full on-device ML stack.
  4. Engage with the web ecosystem—including through the open web standards process—to ensure API designs are high quality and enable cross-browser interoperability.
  5. Contribute directly to the Chromium project for aspects that can be shared with other Chromium-based browsers.

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, or Python

Nice to have

  • Master'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, or Python
  • Bachelor'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 or Python
  • Past experience contributing to a large-scale software engineering project.
  • Past experience with evaluating and training ML models.
  • Solid design, coding, and debugging skills in modern programming languages.

What the JD emphasized

  • on-device ML
  • developers' needs
  • evaluate and optimize cutting-edge on-device models
  • evaluation pipelines and datasets

Other signals

  • on-device ML
  • web APIs
  • developers
  • hundreds of millions of users