The role
From Google's own posting.
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: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
Generate new ideas, keep up with the state-of-the-art in the field, and discuss research directions with other researchers.
Design, rapidly implement, and evaluate ideas, methods, interfaces, and tools to explore new sociotechnical AI systems.
Report and present research findings and developments clearly and efficiently both internally and externally, verbally and in writing.
Suggest and engage in inter and intra-team collaborations to meet ambitious research goals, while also driving significant individual contributions.
Take ownership of substantial technical projects, from ideation and design to implementation and evaluation, often involving cross-functional collaboration.
Minimum qualifications:
PhD degree in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience.
Experience in Python and deep learning frameworks (e.g., JAX, Flax, or Gemma).
Experience in developing multimodal AI models and systems.
Experience conducting research and development, including experimental design, implementation, and analysis.
Publication record in machine learning conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, ECCV).
Preferred qualifications:
Experience fine-tuning and post-training LLMs using Reinforcement Learning (RL) or other alignment methods.
Experience with developing generative AI architectures, techniques, and agentic AI solutions.
Experience with multimodal learning, integrating information from different data types (e.g., vision, audio, text).
Proven expertise in working with, tuning, and prototyping large-scale vision-language models (VLMs) using modern prompting strategies.
Strong interest in and awareness of the AI alignment, safety, responsibility, and fairness landscape, with an excitement to collaborate across disciplines.
PhD degree in Computer Science, Machine Learning, or a related technical field, or equivalent practical experience.
Experience in Python and deep learning frameworks (e.g., JAX, Flax, or Gemma).
Experience in developing multimodal AI models and systems.
Experience conducting research and development, including experimental design, implementation, and analysis.
Publication record in machine learning conferences (e.g., NeurIPS, CVPR, ICML, ICLR, ICCV, ECCV).