Research Engineer, Multimodal Reasoning For Information Literacy

Snapshot At Google DeepMind, our research team is dedicated to tackling the most complex challenges in online information quality. We strive to advance the state of the art by developing innovative solutions to detect manipulated media and misleading narratives, ensuring the integrity of digital discourse. A prominent example of our scientific discovery is Backstory. Our interdisciplinary work spans provenance analysis and the creation of tools for AI-assisted information literacy, leveraging our technologies for the widespread public benefit of a safer online environment. We thrive in a supportive environment that encourages rapid prototyping and iteration, driving our research achievements directly into Google’s flagship models, including Gemini. 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. We’re a dedicated scientific community, committed to “solving intelligence” and ensuring our technology is used for widespread public benefit. We’ve built a supportive and inclusive environment where collaboration is encouraged and learning is shared freely. We don’t set limits based on what others think is possible or impossible. We drive ourselves and inspire each other to push boundaries and achieve ambitious goals. 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 research and build multimodal reasoning systems and Vision-Language Models (VLMs) to assess the trustworthiness of media (images, audio, and videos) on the internet. Key responsibilities: - 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. - Implement tools, libraries, and frameworks to speed up and enable new research. - Report and present research findings, software developments, experimental results, and data analysis clearly and efficiently. - Collaborate with internal and external scientific domain experts. About you In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience: - PhD/Master’s degree in Computer Science, AI, ML, or equivalent practical experience. - At least 2 years of relevant experience developing computer vision techniques or multimodal machine learning models. - Experience in software development using Python and deep learning frameworks (e.g., Jax, TensorFlow, PyTorch), with a proven track record of building high-quality research prototypes and systems. - Quantitative skills in math and statistics. - Experience exploring, analysing and visualising data. In addition, the following would be an advantage: - Experience in training and deployment of large-scale models. - Experience with Video Understanding - Experience with Large Language Models, prompt engineering, few-shot learning, post-training techniques, and evaluations. - A proven track record of research or engineering achievements, such as publications in peer-reviewed conferences or journals. 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. 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 US base salary range for this full-time position is between 174,000 USD - 252,000 USD + bonus + equity + benefits. Your recruiter can share more about the specific salary range for your targeted location during the hiring process.

PhD or Master’s degree in Computer Science, AI, ML, or equivalent practical experience. At least 2 years of relevant experience developing computer vision techniques or multimodal machine learning models. Experience in software development using Python and deep learning frameworks (e.g., Jax, TensorFlow, PyTorch), with a proven track record of building high-quality research prototypes and systems. Quantitative skills in math and statistics. Experience exploring, analysing and visualising data. Additionally advantageous: experience in training and deployment of large-scale models; experience with video understanding; experience with large language models, prompt engineering, few-shot learning, post-training techniques, and evaluations; a proven track record of research or engineering achievements such as publications in peer-reviewed conferences or journals.

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. Implement tools, libraries, and frameworks to speed up and enable new research. Report and present research findings, software developments, experimental results, and data analysis clearly and efficiently. Collaborate with internal and external scientific domain experts.