Principal Software Engineer

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

Principal Software Engineer role focused on improving Copilot search and relevance experiences by adapting vector search algorithms, training relevance models (including LLM fine-tuning and LTR), and building evaluation pipelines. This role involves end-to-end project ownership, innovation in ML/NLP/IR, and cross-organizational collaboration.

What you'd actually do

  1. Drive end-to-end applied science projects
  2. Innovate with scientific rigor
  3. Document, share, and amplify learnings
  4. Translate business goals into scientific strategy
  5. Collaborate across organizations and time zones

Skills

Required

  • 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, JavaScript, or Python

Nice to have

  • Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 10+ years related experience
  • Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 10+ years related experience
  • 10+ years of industrial programming experience in modern languages such as Python, Java, C++, or C#, including production-level ML pipelines
  • Demonstrated expertise in data analysis at scale, including working with logs, telemetry, and large datasets to uncover behavioral patterns, build evaluation datasets, and derive insights
  • Familiarity with modern machine learning and deep learning frameworks such as PyTorch, TensorFlow, scikit-learn, and Hugging Face Transformers
  • Proven ability to collaborate across engineering, product, and science organizations and to communicate technical details clearly to both technical and non-technical stakeholders

What the JD emphasized

  • mission-critical innovations
  • enterprise-scale
  • deep language understanding
  • LLM fine-tuning
  • learning-to-rank (LTR)
  • evaluation pipelines
  • technical depth
  • strategic execution
  • applied science projects
  • enterprise scale
  • semantic search

Other signals

  • delivering mission-critical innovations that directly improve Copilot experiences
  • Adapting advanced vector search algorithms for enterprise-scale semantic retrieval
  • Improving classic and neural keyword search quality through deep language understanding
  • Designing and training relevance models, including LLM fine-tuning and learning-to-rank (LTR) approaches
  • Building robust evaluation pipelines using offline metrics and online A/B experimentation
  • Drive end-to-end applied science projects
  • Innovate with scientific rigor
  • Translate business goals into scientific strategy