At a Glance
- Tasks: Architect and develop groundbreaking generative world models that understand space and physics.
- Company: Join SpAItial, a leader in generative AI and innovative technology.
- Benefits: Competitive salary, inclusive culture, and opportunities for professional growth.
- Other info: Dynamic team environment focused on creativity and collaboration.
- Why this job: Be at the forefront of AI innovation and shape the future of 3D environments.
- Qualifications: PhD in Computer Science with expertise in ML, Vision, or graphics.
The predicted salary is between 60000 - 80000 £ per year.
SpAItial is pioneering the next generation of World Models, pushing the boundaries of generative AI, computer vision, and the simulation of reality. We are moving beyond 2D pixels to build models that natively understand the physics and geometry of our world. Our mission is to redefine how industries, from robotics and AR/VR to gaming and cinema, generate and interact with physically-grounded 3D environments.
We’re looking for individuals who are bold, innovative, and driven by a passion for pushing the boundaries of what’s possible. You should thrive in an environment where creativity meets challenge and be fearless in tackling complex problems. Our team is built on a foundation of dedication and a shared commitment to excellence, so we value people who take immense pride in their work and place the collective goals of the team above personal ambition.
As a part of SpAItial, you’ll be at the forefront of the AI revolution in generative AI technology, and we want you to be excited about shaping the future of this dynamic field. If you’re ready to make an impact, embrace the unknown, and collaborate with a talented group of visionaries, we want to hear from you.
Responsibilities- Architect generative world models that reason about space, time, and physics.
- Design and develop image/video diffusion foundational ML models.
- Large-scale distributed model training on top of distributed cloud infra.
- Develop demos showcasing the trained model prototypes.
- Processing and maintaining large data for model training.
- Productionizing models, test-time model optimization.
- PhD in Computer Science or related field with a top-tier publication record in ML, Vision or graphics (CVPR, ECCV/ICCV, NeurIPS, SIGGRAPH, etc.).
- Strong knowledge of generative models such as image or video diffusion.
- Strong knowledge of cutting-edge architectures including diffusion transformers (DiT).
- Rich experience with deep learning frameworks such as PyTorch.
- Expert coding skills, and ability to rapidly iterate through ML experiments, including usage of modern AI coding tools.
At SpAItial, we are committed to creating a diverse and inclusive workplace. We welcome applications from people of all backgrounds, experiences, and perspectives. We are an equal opportunity employer and ensure all candidates are treated fairly throughout the recruitment process.
Research Scientist - World Models in London employer: spAItial AI
At SpAItial, we are not just redefining the future of generative AI; we are cultivating a vibrant and inclusive work culture that thrives on innovation and collaboration. Our commitment to employee growth is evident through opportunities to work on groundbreaking projects in a dynamic environment, where creativity is encouraged and every team member's contributions are valued. Join us in our cutting-edge location, where you will be at the forefront of technological advancements, surrounded by passionate visionaries dedicated to making a meaningful impact.
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We think this is how you could land Research Scientist - World Models in London
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We think you need these skills to ace Research Scientist - World Models in London
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Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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