Senior Research Engineer, Multimodal Reasoning and Media Forensics, DeepMind

Plan and perform rapid prototyping of computer vision and multimodal machine learning techniques applied to determining authenticity of media information.

Design and train multimodal models capable of complex visual reasoning.

Undertake exploratory analysis to inform experimentation and research directions.

Engage with product teams to drive the development of our research.

Report and present research findings, software developments, experimental results, and data analysis clearly and efficiently.

Minimum qualifications:

PhD in Computer Science, AI, ML, a related field, or equivalent practical experience.
2 years of software development or ML research experience, or education equivalent credit (e.g., internships, student research, pre-graduate working experience) using Python and deep learning frameworks (e.g., Jax and Tensor Flow, PyTorch), and experience building research prototypes and systems.
2 years of experience in training and deployment of large-scale models.
2 years of experience developing computer vision techniques or multimodal machine learning models.

One or more peer-reviewed scientific publication submissions (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, CoRL) or public preprint repositories (arXiv).

Preferred qualifications:

Demonstrated research in LLM reasoning, multimodal systems, RL/agents, robotics, or scientific AI.At Deep Mind, our research team is dedicated to addressing the most complex issues in online information quality. We strive to advance the developing innovative solutions to detect manipulated media and misleading narratives, ensuring the integrity of digital discourse. A prime example of the AI systems you will work on in the media integrity domain is Backstory.

Our interdisciplinary work spans provenance analysis and the creation of models and tools for AI-assisted information literacy, leveraging our technologies for the widespread public benefit of a safer online environment. We grow in a supportive environment that encourages rapid prototyping and iteration, driving our research achievements directly into Google’s flagship models, including  this role, you will need to be passionate about advancing information literacy using machine learning and other computational techniques.

Artificial intelligence will be one of humanity’s most transformative inventions. At Deep Mind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits licants in San Francisco:
Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.

Note:

By applying to this position you will have an opportunity to share your preferred working location from the following:
Mountain View, CA, USA;
San Francisco, CA, USA.PhD in Computer Science, AI, ML, a related field, or equivalent practical experience.
2 years of software development or ML research experience, or education equivalent credit (e.g., internships, student research, pre-graduate working experience) using Python and deep learning frameworks (e.g., Jax and Tensor Flow, PyTorch), and experience building research prototypes and systems.
2 years of experience in training and deployment of large-scale models.
2 years of experience developing computer vision techniques or multimodal machine learning models.

One or more peer-reviewed scientific publication submissions (e.g., NeurIPS, ICML, ICLR, CVPR, ACL, CoRL) or public preprint repositories (arXiv).