Staff Research Engineer, Generative AI, Cloud AI Research, Co-Scientist
Google · Zürich, Switzerland
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
With your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.
Co-Scientist is a multi-agent AI system built on Gemini designed to act as an autonomous virtual scientific colleague. It utilises a generate, debate, and evolve approach to accelerate scientific discovery enabling researchers to synthesize literature, evaluate hypotheses, and optimize experimental designs.
As a Staff Research Software Engineer (R-SWE), you will act as a principal technical bridge, translating scientific AI research into robust, scalable, and intuitive software systems. You will lead the design and implementation of highly multi-agent reasoning models while collaborating with deep learning researchers, product managers, and external scientific partners. You will lead software engineering for the Co-Scientist team in Zurich. The Google Cloud AI Research team addresses AI challenges motivated by Google Cloud’s mission of bringing AI to tech, healthcare, finance, retail and many other industries. We work on a range of unique problems focused on research topics that maximize scientific and real-world impact, aiming to push the state-of-the-art in AI and share findings with the broader research community. We also collaborate with product teams to bring innovations to real-world impact that benefits our customers.
- Own projects end-to-end, from initial scientific research concepts through software design, testing, deployment, and performance analysis.
- Build and deploy agentic AI systems, implementing multi-agent prompting techniques and asynchronous task execution frameworks.
- Drive technical decisions and full-stack software development using Python, C++, and Google's core back-end components.
- Design and establish robust evaluation pipelines, automated test suites, and diagnostic datasets to measure agent performance under extreme scale.
- Collaborate on research with Google DeepMind, Google Research, and university labs to solve complex scientific problems, while mentoring junior engineers and advocating engineering quality.
Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 5 years of experience with software development, including testing and launching software products.
- 3 years of experience optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- 2 years of experience with GenAI techniques (e.g., Large Language Models (LLMs), Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).
Preferred qualifications:
- Master's degree, PhD or Postdoc in Computer Science, Applied Math, Computational Sciences, or a related scientific discipline.
- 3 years of experience with deep learning frameworks (e.g., JAX, TensorFlow, PyTorch) and algorithmic background.
- 1 year of experience in a technical leadership role.
- Experience driving independent, research-adjacent software development and translating academic theories into scalable products.
- Record of technical or scientific contributions to complex, large-scale projects.