Member of Technical Staff (audio)

Microsoft Microsoft · Big Tech · Zürich, ZH, Switzerland · Software Engineering

This role focuses on building and optimizing training data pipelines and evaluation frameworks for audio models, with a secondary focus on model tuning and deployment. The engineer will be responsible for data sourcing, cleaning, labeling, QA, versioning, and lineage, as well as developing evaluation frameworks and driving continuous quality improvements. They will also optimize training and inference performance for latency, throughput, cost, and reliability.

What you'd actually do

  1. Design and maintain training data “recipes” (data sourcing, cleaning, labeling workflows, QA, versioning, and lineage) and develop evaluation frameworks (gold sets, challenge sets, human-in-the-loop evals, regression suites).
  2. Drive continuous quality improvements through systematic error analysis, ablations, and experimentation to improve model performance, robustness, safety, and reliability.
  3. Optimize end-to-end training and inference performance to meet latency, throughput, cost, and reliability targets.
  4. Profile bottlenecks (data loading, preprocessing, GPU utilization, kernel efficiency), implement optimizations (batching, quantization, mixed precision, caching, model distillation, efficient serving patterns).
  5. Work closely with with other members of the AI research team, including researchers, engineers and product managers to define requirements, scope projects, and deliver high-impact solutions.

Skills

Required

  • Master’s degree in computer science OR equivalent technical experience
  • Experience building and maintaining training data pipelines
  • Experience designing eval frameworks and datasets
  • Strong experimentation skills
  • Dedication to writing clean, maintainable, and well-documented code
  • Demonstrated interpersonal skills and ability to work closely with cross-functional teams

Nice to have

  • PhD is a plus
  • Contributions or interest in audio
  • Passion for learning new technologies
  • Ability to work in a fast-paced environment

What the JD emphasized

  • training data pipelines
  • evaluation frameworks
  • model performance
  • inference optimization

Other signals

  • training data pipelines
  • evaluation frameworks
  • model performance
  • inference optimization