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.

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, along with building large scale systems to demonstrate effectiveness of our research ideas. We specifically focus on advancements in 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!

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, contributing to our cutting edge models/products, while also disseminating our findings through publications at top ML conferences/journals.  

Research Engineers at Google DeepMind lead our efforts in developing and productionizing large scale foundational models towards the end goal of solving and building Artificial General Intelligence. 

In particular, your role would be to help design, implement and experiment with various research ideas and hypotheses in large foundational models space, with emphasis on efficiency and adaptivity. After designing the kernel of a research idea, the next step would be to further polish and refine the idea, and productionize it for some of the key ML models for Google. 

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). 
• Productionize promising research ideas and ensure that the core techniques can be leveraged by multiple internal and external teams.  
• Suggest and engage in team collaborations to meet ambitious research and productionisation goals.
• 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 Engineer at Google DeepMind India, we look for the following skills and experience:

• BSc, MSc or PhD/DPhil degree in computer science, mathematics, applied stats, machine learning or similar experience working in industry
• Proven knowledge and experience of Python or C++
• Deep knowledge of algorithm design 
• Proven track record of engineering and productionizing large scale systems and working with multi-stakeholder environments.  
• Strong communication and interpersonal skills

In addition, the following would be an advantage:

• Knowledge of machine learning and statistics 
• Proven experience with ML frameworks (e.g. JAX) 
• Proven experience with large multimodal model training 
• 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!