Computer Vision Research Scientist

Computer Vision Research Scientist Overview

This is an opportunity to join a global technology organisation that is recognised for leadership in artificial intelligence and advanced computing. The company places a strong emphasis on research, innovation, and international collaboration, and it works with academic and industry partners to create next-generation intelligent systems. Its London AI research group is growing and is looking for a research scientist focused on computer vision to help push forward work in multimodal artificial intelligence.

The role sits within a highly experienced international team and offers the chance to contribute to pioneering research across computer vision, multimodal learning, and foundation models. The work is not limited to theory: the team also aims to translate research into practical applications that have real impact. This is a permanent, full-time position based in Central London.

What you will do

• Take part in frontier AI research focused on computer vision and multimodal systems.
• Design and develop Vision Transformer-based and other multimodal architectures that improve reasoning, efficiency, and scalability.
• Advance methods for multimodal representation learning, alignment, and long-context modelling.
• Investigate approaches for training large multimodal foundation models at scale.
• Work to improve model performance, robustness, and generalisation across different tasks and use cases.
• Source, process, and organise large multimodal datasets containing combinations of images, video, audio, and text.
• Build reliable workflows for cleaning, filtering, annotating, and quality-checking data.
• Maintain reproducible datasets through careful versioning and documentation.
• Improve data sampling strategies and use iterative evaluation to raise dataset quality.
• Develop distributed training systems that can support large-scale multimodal model development.
• Optimise GPU usage, compute scheduling, and training efficiency.
• Contribute to training and inference frameworks that make model development more scalable.
• Help improve the reliability, speed, and scalability of the AI infrastructure.
• Apply multimodal AI capabilities to intelligent products and user-facing applications.
• Work closely with engineering and product teams to move research ideas into production.
• Support the ongoing deployment and refinement of advanced AI technologies.

What the employer is looking for

• A degree in Computer Science, Mathematics, Statistics, Artificial Intelligence, or a similar technical discipline.
• Strong Python programming ability, with hands-on experience using PyTorch and modern deep learning tools.
• Excellent algorithmic thinking, mathematical reasoning, and problem-solving skills.
• Strong communication and teamwork skills, with the ability to work across multidisciplinary groups.
• A proactive, self-motivated mindset and enthusiasm for challenging research work.
• Interest or experience in computer vision, multimodal learning, or foundation model research.
• Desirable: publication record at leading AI or computer vision conferences such as CVPR, ICCV, ECCV, NeurIPS, ICML, or ICLR.
• Desirable: experience training or fine-tuning large-scale vision, language, or multimodal models.
• Desirable: open-source AI contributions or research experience gained in industry or academic laboratories.

What is on offer

• A permanent, full-time role.
• A position based in Central London.
• The chance to work for a global company operating at the forefront of AI and advanced computing.
• Involvement in a research team with strong international expertise.
• The opportunity to contribute to cutting-edge multimodal AI research.
• The ability to help bring research innovations into real-world products and applications.
• Collaboration with leading academic and industry partners.

For further information, candidates are invited to contact Charles Duran. Applications can be submitted through the Apply Now route on the listing.

Degree Requirement: Bachelor’s

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