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
At Google DeepMind, we've built a unique culture and work environment where long-term ambitious research can flourish. The multimodal team in Zurich investigates new architectures, approaches, and applications of foundational multimodal models. We are particularly interested in the interaction between these and data-centric aspects of training large-scale models. We believe there is a virtuous circle in which novel datasets enable novel models, and gaps in performance inspire new datasets.
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.
The Role
Research Scientists on the Multimodal team lead our efforts to advance architectural and data-centric approaches for multimodal models. This includes developing innovative data recognition, construction, optimization, and evaluation techniques to drive progress in the field. This work is intimately linked with the training of multimodal models, where each informs and refines the other.
We are looking for researchers who thrive in an interdisciplinary environment. Previous experience with multimodal, data centric research, or other parts of machine learning is valued.
Key responsibilities:
- Develop and evaluate methods for curating high-quality multimodal datasets, including collection, filtering, synthesis, annotation, and establishing evaluation metrics.
- Improve large-scale multimodal systems using data-centric approaches, such as developing novel datasets and tasks, optimizing data mixes, and refining loss functions.
- Design and build benchmarks and evaluation frameworks to assess multimodal models across diverse tasks and domains.
- Ensure inclusive, unbiased datasets and mitigate ethical risks associated with data collection and model development.
- Clearly communicate research findings and collaborate with Google teams for wider impact.
- Collaborate effectively to achieve ambitious research goals.
About You
In order to set you up for success as a Research Scientist at Google DeepMind, we look for the following skills and experience:
- PhD in machine learning, artificial intelligence or computer science.
- Research interests in data and novel dataset creation.
- Familiarity with experiment design principles.
- Experience with deep learning research.
- Strong coding and communication skills.
In addition, the following would be an advantage:
- Experience with multimodal modelling.
- A proven track record of publications in top-tier conferences.
- Understanding of data privacy and ethical considerations in data collection and use.
Application deadline: 4th February 5:00pm GMT
Top Skills
What We Do
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.
Our long term aim is to solve intelligence, developing more general and capable problem-solving systems, known as artificial general intelligence (AGI).
Guided by safety and ethics, this invention could help society find answers to some of the world’s most pressing and fundamental scientific challenges.
We have a track record of breakthroughs in fundamental AI research, published in journals like Nature, Science, and more.Our programs have learned to diagnose eye diseases as effectively as the world’s top doctors, to save 30% of the energy used to keep data centres cool, and to predict the complex 3D shapes of proteins - which could one day transform how drugs are invented.
