Member of Technical Staff - Principal Backend Engineer, Copilot Memory and Personalization

Microsoft Microsoft · Big Tech · Mountain View, CA +1 · Software Engineering

Backend Engineer role focused on building the data foundations for Copilot's memory and personalization features. This involves designing and operating large-scale data pipelines for signal ingestion, normalization, enrichment, aggregation, and memory generation, enabling downstream ML and product consumers. The role also involves technical leadership in data systems and influencing architecture.

What you'd actually do

  1. Design and evolve large-scale data architectures that support Copilot memory and personalization, spanning batch, streaming, and serving paths.
  2. Build and operate high-quality personalization and memory data pipelines, including signal ingestion, normalization, enrichment, aggregation, memory generation, and full lifecycle management.
  3. Enable memory and personalization features by exposing well-designed datasets, APIs, and feature interfaces for downstream product and ML consumers.
  4. Work closely with PMs, applied ML, and product engineering to translate product intent into robust data systems and measurable outcomes.
  5. Act as a technical leader for memory and personalization data systems, influencing architecture and standards across multiple teams.

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

  • Master's Degree in Computer Science or related technical field
  • 12+ years technical engineering experience
  • 15+ years technical engineering experience
  • coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, OR Python
  • Thrive in a fast-paced, collaborative environment and are comfortable making progress in ambiguity.
  • Experience building and deploying machine learning or large language model (LLM) applications at scale.
  • Experience designing and implementing large-scale embedding, retrieval, and ranking systems.

What the JD emphasized

  • large-scale data architectures
  • personalization and memory data pipelines
  • machine learning or large language model (LLM) applications at scale
  • large-scale embedding, retrieval, and ranking systems

Other signals

  • building foundations of Copilot memory and personalization
  • designing systems that reliably capture, refine, and serve user signals across interactions
  • deepen memory with every interaction
  • personalize experiences around individual goals and preferences
  • large-scale data architectures that support Copilot memory and personalization
  • personalization and memory data pipelines
  • exposing well-designed datasets, APIs, and feature interfaces for downstream product and ML consumers
  • Experience building and deploying machine learning or large language model (LLM) applications at scale
  • Experience designing and implementing large-scale embedding, retrieval, and ranking systems