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.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $174000 - $253000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Develop new modeling solutions for obtaining better quality-efficiency tradeoffs in generative models.
- Translate research ideas into Gemini production codebase by solving challenging engineering tasks.
- Implement new architecture modifications in Gemini codebase, working with pre-training and post-training pipelines, conducting experiments, identifying new directions and proposing solutions.
Qualifications
Minimum qualifications:
- PhD degree in Computer Science or Computer Engineering, similar technical field of study (e.g., Electrical Engineering, Mathematics, Information Technology) or equivalent practical experience.
- 2 years of experience with one or more machine learning frameworks (e.g., PyTorch, JAX, or TensorFlow).
- Experience with Generative AI, Generative AI Agent, Natural Language Processing, Large Language Model.
Preferred qualifications:
- 3 years of experience applying and productionizing large visual, language, and multimodal research.
- 1 year of experience leading research efforts and influencing other researchers.
- Experience developing, fine-tuning, and deploying generative AI and Large Language Models (LLMs) in enterprise environments.