Research Scientist

At Google DeepMind, we value diversity in terms of experience, knowledge, backgrounds, and perspectives, using these qualities to create significant impact. We are committed to equal employment opportunity regardless of sex, race, religion or belief, ethnicity or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy, or any other related condition 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: We are looking for a Research Scientist to join our team to research the development of next-generation search and language modeling technologies to power safe and accurate generative information systems. About Us: Artificial Intelligence could be one of humanity’s most useful inventions. We are a team of scientists, engineers, machine learning experts among others, working together to advance the state of the art in artificial intelligence. We use our technologies for public benefit and scientific discovery while also collaborating with others on critical challenges, ensuring safety and ethics are the highest priority! The Role: Within the team, Research Scientists are encouraged to lead/support a problem-driven research agenda aimed at producing both fundamental and practically useful technological advances in the organization of information through AI. The expectation is to conduct novel research that balances long-term research agendas with short-term value for users. Key Responsibilities: 1. Lead the ideation and development of new capabilities of multimodal LLM-based systems for conversational search and question answering. 2. Collaborate with research engineers in developing ambitious prototypes. 3. Report findings in academic conferences and journals. About You: For success as a Research Scientist at Google DeepMind, we seek the following skills and experience: 1. A PhD in a relevant technical field or equivalent practical experience. 2. Experience in a research domain connected to generative models. 3. Expertise with LLMs fine-tuning, in-context learning, prompt optimization. The following experience would be an advantage: 1. Experience with multimodal LLMs. 2. Familiarity with human evaluation design, conducting human data collections and analysis, and scalable oversight and alignment problems. 3. Experience with open-ended learning, RL, RLHF, RLAIF, preference optimization, constitutional AI etc. Applications will close on Thursday 12th October at 5pm BST and will be reviewed on a rolling basis.