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Software Engineer, GenAI, DeepMind

Google
1 day ago
Full-time
On-site
Zürich, ZH

JobsCloseBy Editorial Insights

Google DeepMind seeks a Software Engineer in GenAI for a full-time onsite role in Zürich, joining a team that combines research modeling with product engineering across GDM and Search. The role blends low-level algorithm work with AI/ML modeling, infrastructure, and user-facing solutions, requiring a bachelor’s degree in CS or equivalent and at least 8 years in the full lifecycle of research modeling. Candidates should excel at solving complex problems, prototyping rapidly, and working on ill-defined problems across multiple codebases and tools. To apply, highlight end-to-end impact: concrete examples of novel algorithms or prototypes, collaboration with product teams, and measurable results, plus evidence of learning agility and ethical focus.

Minimum qualifications:

  • Bachelor's degree in Computer Science, a relevant technical field with a focus on AI research, or equivalent practical experience.
  • 8 years of experience in the full lifecycle of research modeling.
  • Experience working with two or more of the following: advanced algorithms, machine learning, information retrieval, natural language processing, data science, distributed and parallel systems, developing software systems.

Preferred qualifications:

  • Ability to solve exceptionally complex problems (e.g., math and programming competitions or developing novel algorithms).
  • Exceptional hacking skills and ability to quickly prototype and iterate on complex systems.
  • Passion for working on real problems, a determination to overcome obstacles, and the ability to work on any task to get the job done.
  • Quick learner with strong analytical and coding skills.
  • Willingness to learn multiple tools and codebases.
  • Willingness to work on ambiguous, ill-defined problems, refining final goals as new information is learned.

About the job

Our team consists of ICs, who work across project boundaries. We initiate, prototype, hack, partner, and co-lead numerous high-profile projects within GDM, Search, and beyond. Our work spans a range, including low-level algorithms and infrastructure, AI/ML modeling and ranking, fundamental research, and product development. We consistently mix SoTA research and practical engineering.

Our team has been responsible for significant improvements in Search algorithms for multiple generations, creating the ranking and machine learning platform, inventing novel algorithms and model architectures, and much more.

This role involves developing novel algorithms and AI architectures, hacking and prototyping, and creating tools to solve the most challenging problems, from infrastructure to user-facing products.

This role offers the opportunity to work on a wide array of challenging problems and cutting-edge fundamental research while directly applying solutions to Google, making a significant impact on Google users.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Our team has been responsible for significant improvements in Search algorithms for multiple generations, creating the ranking and machine learning platform, inventing novel algorithms and model architectures, and much more.
We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

Responsibilities

  • Develop a wide range of solutions, spanning from modeling to infrastructure (e.g., storing new knowledge, efficiently using LLMs to evaluate thousands of requests, improving those models, and enhancing their intelligence).
  • Work with multiple codebases and machine learning libraries, creating numerous throwaway models and prototypes, as well as reusable model architectures and advanced libraries.
  • Work in the context of real applications for important Google products and in partnership with product teams and other research engineers.
  • Develop novel and practical solutions for AI and AI-powered tools. Expect to work on ambiguous, ill-defined problems, move quickly, iterate rapidly, and handle complex problems and codebases.