Staff Software Engineer, Labs Science

Google Google · Big Tech · London, United Kingdom

Staff Software Engineer at Google Labs, focused on translating breakthrough GenAI research into user-centric products for scientific discovery. The role involves architecting and building new products, designing agentic systems, and establishing frameworks for model performance measurement and fine-tuning.

What you'd actually do

  1. Partner with Google DeepMind to translate breakthrough GenAI research assets like Co-Scientist and AlphaEvolve into intuitive products that accelerate scientific discovery.
  2. Architect and build entirely new products from the ground up, moving rapidly from prototypes to production-ready services in a changing environment.
  3. Design and implement sophisticated agentic systems and multi-step AI prompts to solve complex, open-ended scientific tasks using the latest LLM reasoning advancements.
  4. Establish frameworks and metrics to measure model performance, driving continuous quality improvements and model fine-tuning for specific scientific use cases.
  5. Work closely with Product Managers and UX partners to discover user needs and iterate on features that genuinely delight the scientific community.

Skills

Required

  • C++
  • Java
  • Kotlin
  • Typescript
  • front-end frameworks
  • full-stack development
  • API development
  • software design
  • architecture
  • machine learning
  • agentic coding

Nice to have

  • TypeScript
  • JavaScript
  • Google Cloud Platform (GCP)
  • billing/infrastructure for compute-intensive workloads

What the JD emphasized

  • translate breakthrough research
  • agentic systems
  • multi-step AI prompts
  • model performance
  • model fine-tuning

Other signals

  • Google Labs incubates early-stage efforts to discover and create Google’s next generation of AI products.
  • transform breakthrough research—such as Co-Scientist and AlphaEvolve—into user-centric products designed to accelerate the pace of scientific discovery.
  • Design and implement sophisticated agentic systems and multi-step AI prompts to solve complex, open-ended scientific tasks using the latest LLM reasoning advancements.
  • Establish frameworks and metrics to measure model performance, driving continuous quality improvements and model fine-tuning for specific scientific use cases.