Post-Doctoral Researcher, PhD, 2026 Start, DeepMind (Fixed-Term Contract) -
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Please complete your application before July 31, 2026.
To start the application process, you will need an updated CV or resume and a current unofficial or official transcript in English. Click on the “Apply” button on this page and provide the required materials in the appropriate sections (PDFs preferred):
In the “Resume Section:” attach an updated resume. Please ensure you include the following in it, all consolidated in a single PDF:
Research work: Projects, Publications, Research experience and References (if any)
Statement of purpose (no more than 500 words), detailing reasons for your interest in the Post-Doctoral Researcher role.
Please ensure you’ve listed your graduation date (in MM/YY)
In the “Education Section:” attach a current or recent unofficial or official transcript in English. Under “Degree Status,” select “Now attending/Graduated” to upload a transcript.
If you think this role interests you, please apply to this role.
Minimum qualifications:
- PhD degree in a STEM field (e.g., Computer Science, Mathematics, or Statistics) or equivalent practical experience.
- Experience with one or more general purpose programming languages: Python, C/C++, or Java, etc. and training deep learning models in frameworks such as JAX, TensorFlow, or PyTorch.
Preferred qualifications:
- Research experience in machine learning or AI techniques (e.g. open source project(s), campus lab experience, research internship(s), or publication(s)).
- A strong publication record in conferences/journals.
- Ability to work and collaborate across multiple teams and work with ambiguity.
About the job
The Post-Doctoral Researcher role is a 12 to 24-month role designed to explore challenging problems for pushing frontiers of AI, while collaborating with Google DeepMind scientists and engineers. Your role will entail identifying and abstracting out impactful problem definitions, designing novel algorithms to solve the defined problems, and demonstrating effectiveness of the techniques at scale, with potential application to products that might have a positive impact on billions of users.
At Google DeepMind, we're a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence, and fulfill our mission of building AI responsibility to benefit humanity. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.
In particular, our ML Optimization (MLO) team at GDM India, has deep expertise in machine learning fundamentals, large foundational models, reinforcement learning, generative modeling, and causal inference. Some of our breakthrough technologies include Matryoshka Representations and Matformers, Tandem Transformers, Treeformer, Causal Representation Learning, and meta-reasoning of models. We have been at the forefront of reimagining Google's latest foundational models from an efficiency and adaptability viewpoint, contributing to our models/products, while also disseminating our findings through publications at top ML conferences/journals.
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.
Responsibilities
- Work with research mentors to formulate research projects or novel applications of machine learning aligned with the team's mission.
- Conduct research and publish high quality work.
- Design and execute large-scale experiments, writing high-quality, reusable code, and contributing to a production-ready system using frameworks such as JAX.
- Learn and understand research in machine learning algorithms.
- Understand how to use research to drive design decisions.
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