Research Scientist, Generative Media and Multimodal LLM

About the job

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.

As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

In this role, you will conduct research on multimodal LLM and agentic workflows for generative media. Collaborating closely with visual generation researchers, you will focus primarily on the language and reasoning side of multimodal generation and multi-turn editing-designing and post-training models that understand nuanced user intent, ground generation using search and external tools, evaluate output quality via reward models and automated judges, and generalize across various languages and writing systems.

Google Research is building the next generation of intelligent systems for all Google products. To achieve this, we’re working on projects that utilize the latest computer science techniques developed by skilled software developers and research scientists. Google Research teams collaborate closely with other teams across Google, maintaining the flexibility and versatility required to adapt new projects and foci that meet the demands of the world's fast-paced business needs.

Responsibilities

• Conduct fundamental and applied research on multimodal LLMs and agentic workflows for generative media, including multi-turn reasoning, planning, instruction following, and tool use.
• Design, prototype, and run experiments on post-training and alignment methods (such as fine-tuning, reinforcement learning, and synthetic data curation) to improve model quality, faithfulness, and controllability.
• Define data pipelines, benchmarks, reward models, and evaluation metrics for multimodal generation and interactive editing under minimal guidance, refining hypotheses through empirical analysis.
• Identify and investigate research opportunities in multilingual, cross-lingual, and cross-script multimodal modeling, collaborating with internal teams to shape research strategy and expand impact.
• Author research papers to share findings in the research community, and partner with Engineering teams to transfer promising research into Google products.

Qualifications

Minimum qualifications:

• PhD in Computer Science, a related field, or equivalent practical experience.
• Experience with research projects in Large Language Models (LLMs), Natural Language Processing (NLP), or Multimodal Machine Learning.
• One of more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).

Preferred qualifications:

• 2 years of coding experience.
• 1 year of experience owning and initiating research agendas.
• Research experience or publications in LLM agents (planning, reasoning, tool use, retrieval-augmented generation), multimodal vision-language models, multilingual NLP, or post-training and alignment (fine-tuning, reinforcement learning, reward modeling).