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Salary
≈ $131k – $301k per year (Estimated)
Location
In office (Mountain View)
Seniority
Junior · 2+ years exp

Confirmed on the employer's own hiring board on Oct 1, 2026. First seen by Alion on Sep 30, 2026.

Overview
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Impact
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Google DeepMind is the artificial intelligence research and engineering division of Alphabet, formed in 2023 by merging the London laboratory founded in 2010 with the Google Brain team. It builds frontier foundation models such as the Gemini family, generative media systems including Veo and Imagen, and scientific tools like AlphaFold, whose protein structure predictions earned a share of the 2024 Nobel Prize in Chemistry. The unit pairs long-horizon research on reinforcement learning and general intelligence with product delivery across Google Search, Workspace, Android and Google Cloud.

About the job

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 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

  • Bring a combination of engineering and research expertise to advance Gemini's multimodal understanding and generation capabilities.
  • Design, implement, and scale state-of-the-art Large Language Models (LLM's) core image/video understanding capabilities.
  • Drive end-to-end experimental workflows across pre-training and post-training.
  • Build robust, scalable infra and data pipelines to support high-quality multimodal training and eval benchmarks.
  • Partner closely with Research Scientists and Software engineers (SWEs) to transition cutting-edge research prototypes into production-ready foundation models, striking the right balance between rapid prototyping, maintainability, and generality.

Qualifications

Minimum qualifications:

  • PhD degree in Computer Science, a related field, or equivalent practical experience.
  • 2 years of experience leading a research agenda and influencing other researchers.
  • 2 years of experience in an applied research setting.
  • Experience in software engineering, including coding, code reviews, design discussions and reviews.
  • Experience in Artificial Intelligence or Machine Learning.

Preferred qualifications:

  • Experience with the GenAI techniques (e.g., LLMs, multimodal, large vision models) or with genAI-related concepts (e.g., evaluations, language modeling, computer vision).
  • A proven track record of research or engineering achievements, such as publications in peer-reviewed conferences or journals.
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