Research Scientist, Multimodal Generative AI, Google DeepMind

Google; Singapore; On-site; SGD 80,000 - 120,000; Full time

Job summary

A leading AI research organization in Singapore is seeking a Research Scientist to work on groundbreaking generative AI. In this role, you will design and evaluate deep learning algorithms, present research findings, and work closely with cross-disciplinary teams. Candidates should have a PhD in a relevant field and at least 2 years of research experience in the areas of deep learning and generative AI. This role offers a unique opportunity to contribute to ethical advancements in AI.

Qualifications

• PhD in relevant fields like AI or Machine Learning.
• 2+ years of deep learning research and development.
• Experience with language and deep learning libraries.

Responsibilities

• Design and evaluate deep learning algorithms for generative AI.
• Present research findings clearly to stakeholders.
• Drive collaborations to achieve research goals.

Skills

Deep learning research

Generative AI techniques

Software development

Collaboration

Education

PhD in Computer Science or related field

Tools

Python

Jax

TensorFlow

PyTorch

Job description

Job Description

Our team works on developing state-of-the-art methods for AI generative media models, with a particular focus on culturally-adapted image and video generation.

At Google DeepMind, we've built a unique culture and work environment where long-term ambitious research can flourish. Our special interdisciplinary team combines the best techniques from deep learning, reinforcement learning, and systems neuroscience to build general-purpose learning algorithms. We have already made a number of high-profile breakthroughs towards building artificial general intelligence, and we have all the ingredients in place to make further significant progress over the coming year!

Research Scientists lead our efforts in developing novel tools, infrastructure, and algorithms towards the end goal of solving and building Artificial General Intelligence.

Having pioneered research in the world's leading academic and industrial labs, PhDs, post‑docs, or professorships, Research Scientists join Google DeepMind to work collaboratively within and across Research fields. They are expected to work with teams on large scale AI, and develop solutions to fundamental questions in machine learning and AI.

Drawing on expertise from a variety of disciplines including deep learning, computer vision, language modeling, and advanced generative architectures, our Research Scientists are at the forefront of groundbreaking research.

Job responsibilities

• Design, rapidly implement, and rigorously evaluate cutting‑edge deep learning algorithms and data curation for multimodal generative AI, with a particular emphasis on culturally‑adapted image and video synthesis.
• Report and present research findings and developments clearly and efficiently both internally and externally, verbally and in writing.
• Suggest and engage in team collaborations to meet ambitious research goals, while also driving significant individual contributions.
• Work in collaboration with our Ethics and Governance teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

Minimum qualifications

• PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Vision, or equivalent practical experience.
• 2+ years of relevant experience in deep learning research and development, particularly in generative AI and related to image and video synthesis. This includes diffusion models and autoregressive generative models.
• Experience in software development with one or more programming languages (e.g., Python) and deep learning frameworks (e.g., Jax, TensorFlow, PyTorch), with a track record of building high-quality research prototypes and systems.

Preferred qualifications

• Demonstrated experience in large-scale training of multimodal generative models.
• A track record of research or engineering achievements, including publications in peer-reviewed conferences or journals.