Research Scientist, Information Quality

Snapshot

This role requires a passion for advancing information literacy through AI & machine learning, focusing on assessing media trustworthiness (images, audio, and video) and exploring concepts like authenticity, provenance, and context. Key responsibilities include formulating metrics, simulations, rapid prototyping of ML techniques, exploratory data analysis, collaborating with product teams to drive research, and developing tools and frameworks to accelerate research. A public example of research work is Backstory.

The Role

To succeed in this role, you will need to be passionate about advancing information literacy using machine learning and other computational techniques. You'll join an interdisciplinary team of domain experts, ML researchers, and engineers to conduct cutting-edge research and advance the next generation of multimodal AI assistants that help co-investigation and deliberation. Relevant domains may include, but are not limited to, determining media authenticity, context discovery, and open source intelligence investigations. A public example of recent work is Backstory.

Key responsibilities:

• Drive the projects by defining key research questions.
• Design, implement, and evaluate experiments to provide clear answers
• Contribute to real world impact, by landing your research in Google products and services.
• Publish research findings in top academic conferences and journals
• Stay up-to-date with the latest advancements in the field
• Collaborate with internal and external scientific domain experts.

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 Computer Science, Statistics, or a related field.
• Strong publication record in top machine learning and/or computer vision conferences or journals (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV).
• Expertise in one or more of the following areas: social impact of AI, reinforcement learning, multimodal agents, computer vision, natural language understanding.

In addition, the following would be an advantage:

• Passion for research on societal benefits and implications of the internet and AI with focus in information literacy.
• Experience with training, evaluating, and interpreting large language models.
• Experience working with large and noisy datasets.
• Experience collaborating across fields.
• Proven ability to design and execute independent research projects.

When assessing technical background we will take a holistic view of the mix of scientific, ML and computational experience. We do not expect you to be an expert in all fields simultaneously.