Staff Software Engineer, Knowledge Catalog Search and Discovery

Google Google · Big Tech · Sunnyvale, CA +2

Staff Software Engineer at Google Cloud focused on the Knowledge Catalog's search experience, owning the end-to-end ML lifecycle for search relevance. This involves designing, prototyping, and testing information retrieval techniques, training and tuning models based on user query logs and catalog metadata, and integrating these models into high-throughput, low-latency search infrastructure.

What you'd actually do

  1. Design, develop, test, deploy, maintain, and enhance large scale software solutions.
  2. Provide technical leadership on high-impact projects. Manage project priorities, deadlines, and deliverables.
  3. Facilitate alignment and clarity across teams on goals, outcomes, and timelines. Influence and coach a distributed team of engineers.
  4. Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide the development of model optimization and data processing strategies.

Skills

Required

  • software development
  • software products
  • software design and architecture
  • Speech/audio
  • reinforcement learning
  • ML infrastructure
  • ML design
  • ML frameworks (e.g., TensorFlow, PyTorch, or JAX)

Nice to have

  • Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
  • data structures/algorithms
  • technical leadership role leading project teams and setting technical direction.
  • complex, matrixed organization involving cross-functional, or cross-business projects.
  • modern search architectures, including two-stage retrieval systems (bi-encoders and cross-encoders), semantic search, and vector embeddings

What the JD emphasized

  • end-to-end ML lifecycle for search relevance
  • training and tuning models
  • high-throughput indexing and retrieval pipelines
  • sub-100ms response times
  • modern search architectures, including two-stage retrieval systems (bi-encoders and cross-encoders), semantic search, and vector embeddings

Other signals

  • ML lifecycle for search relevance
  • training and tuning models
  • high-throughput indexing and retrieval pipelines
  • sub-100ms response times