Member of Technical Staff - Full Stack Software Engineer

Microsoft Microsoft · Big Tech · Mountain View, CA +2 · Software Engineering

Full Stack Software Engineer to build capabilities for Microsoft's personalized AI assistant, Copilot. The role involves working across the evaluation platform, including data sampling, collection, processing, analysis, and insight generation. Responsibilities include full-stack development, prompt engineering, leveraging AI tools, and collaborating with teams to assess Copilot's performance, trustworthiness, and visual appeal across various platforms and scenarios like multi-turn conversations with voice input.

What you'd actually do

  1. Expertise in experimentation methodologies, including A/B evaluation, data sampling, measurement techniques, evaluation design, and data analysis.
  2. Demonstrating strategic vision by understanding organizational goals, translating metrics into actionable insights, and enhancing product quality.
  3. Designing pipeline architecture to ensure rapid iteration and scalability.
  4. Conducting post-analysis of labeled data and developing dashboards to visualize insights.
  5. Collaborating closely with the product team to enhance quality and address gaps.

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#, Java, or Python OR equivalent experience.

Nice to have

  • SQL, PostgreSQL or MySQL
  • building scalable services on top of public cloud infrastructure like Azure, AWS, or GCP
  • extensive use datastores like RDBMS, key-value stores, etc.
  • Development & Debugging with dev environments like Visual Studio or Visual Studio Code
  • HTML, CSS, JavaScript, ASP.NET, REST, jQuery
  • browser automation tools like Selenium, Puppeteer or Playwright
  • mobile automation tools like Appium
  • LLMs and AI ChatBots
  • Prompt Engineering
  • Azure DevOps, GIT
  • Azure Open AI, Azure Foundry
  • leading technical projects and supporting architectural decisions with data.

What the JD emphasized

  • evaluation platform
  • data sampling
  • AI data collection and processing
  • data analysis and evaluation
  • insight generation
  • prompt engineering
  • Copilot’s relevance, performance, trustworthiness, and visual appeal
  • multi-turn conversations with voice input

Other signals

  • evaluation platform
  • AI data collection and processing
  • data analysis and evaluation
  • insight generation
  • prompt engineering
  • Copilot
  • relevance, performance, trustworthiness, and visual appeal
  • multi-turn conversations with voice input
  • consumer AI products and research