As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.
As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.
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.
Responsibilities
- Drive novel and disruptive research to advance frontier AI models, particularly within text diffusion, by identifying and solving key scientific challenges.
- Prototype and develop new architectures and algorithms, contributing directly to Gemini Diffusion research efforts.
- Validate the theoretical and practical impact of research at scale through experimental design, execution, and in-depth analysis.
- Advance the fundamental architecture, algorithmic design, and capabilities of large-scale diffusion models.
- Collaborate with other Generative AI teams to transition research technologies into production, fostering a culture of deep scientific expertise and accuracy.
Qualifications
Minimum qualifications:
- PhD degree in Computer Science, Electrical Engineering, a scientific discipline, or Mathematics, a related field, or equivalent practical experience.
- 2 years of experience conducting research in machine learning, evidenced by publications or prior research roles.
- Experience in programming with Python-based scientific libraries (e.g., NumPy, SciPy, JAX, or TensorFlow).
- Experience with language models (LLMs), transformers, diffusion models, text diffusion, or distributed training.
- Experience with machine learning, mathematics, and statistics (e.g., linear algebra or calculus).
Preferred qualifications:
- Experience in C++ or broader programming.
- Experience in data engineering and visualization.
- Experience in large-scale system design and distributed systems.
- Strong communication skills, including experience in technical discussions, presentations, and research writing.
- A track record of building software, either in open source or as part of a company product or research papers.
- Distributed computation for ML, especially in the context of accelerators (e.g., sharding, multi-host computation).