Research Scientist

Research Scientist:

At Google DeepMind, we value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or related condition (including breastfeeding) or any other basis as protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.

Snapshot

The Machine Learning and Optimization (MLO) team at Google DeepMind India is driven by the mission of enabling ultra-efficient, adaptable, and performant large models for everyone .

Our mission is realized through foundational research in machine learning, specifically focusing on advancements in next-generation machine learning architectures, large-scale optimization algorithms, reinforcement learning methodologies, and innovative sampling techniques.

We apply our research advances to critical product launches in Google, touching the lives of hundreds of millions of users, and we are looking forward to doing more!

About us

Artificial Intelligence could be one of humanity s most useful inventions. 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. 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 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. We have been at the forefront of reimagining Google s latest foundational models from an efficiency and adaptability viewpoint, while also disseminating our findings through publications at top ML conferences/journals.

The role

Research Scientists at Google DeepMind lead our efforts in developing novel algorithmic architecture towards the end goal of solving and building Artificial General Intelligence.

Having pioneered research in the worlds leading academic and industrial labs in PhDs, post-docs or professorships, Research Scientists join Google DeepMind to work collaboratively within and across Research fields. They develop solutions to fundamental questions in machine learning and AI.

Key responsibilities

• Design, implement and evaluate models, agents and software prototypes of large foundational models.
• Deep dive into fundamentals of both the ML aspects of foundational models (like architectures, loss functions, data, evals) as well as their implementation on neural accelerators (efficiency during training, serving).
• Report and present research findings and developments including status and results clearly and efficiently both internally and externally, verbally and in writing.
• Suggest and engage in team collaborations to meet ambitious research goals.
• Work with external collaborators and maintain relationships with relevant research labs and key individuals as appropriate.
• Work in collaboration with our Responsible AI teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

About you

In order to set you up for success as a Research Scientist at Google DeepMind India, we look for the following skills and experience:

• PhD in a technical field or equivalent practical experience.

In addition, the following would be an advantage:

• PhD in Machine Learning, Computer Vision, Natural Language Processing or related areas
• A proven track record of publications and relevant experience in one or more areas such as large foundational models, ML+Systems, optimization, learning theory, computer vision, NLP etc.
• Proven experience with ML frameworks (e.g. JAX) and proven experience with training large models
• Proven experience working in industry, working on projects from proof-of-concept through to implementation, applying experimental ideas to applied problems
• A real passion for AI, Optimization, and Efficiency!