Research Scientist - World Models
Entwickelt und trainiert generative 3D-Weltmodelle und Diffusionsmodelle für Bild und Video, inklusive verteiltem Training und Produktivsetzung.
Entwickelt und trainiert generative 3D-Weltmodelle und Diffusionsmodelle für Bild und Video, inklusive verteiltem Training und Produktivsetzung.
Your mission dida is a machine learning software company with exciting problems for instance in computer vision and natural language processing.
Your mission You will - work on machine learning research projects, in particular addressing the question of how machine learning methodolog
Master s degree or PhD in Computer Science, Information Systems, Robotics, Computer Vision, or a related field.
SpAItial is pioneering the next generation of World Models, pushing the boundaries of generative AI, computer vision, and the simulation of reality.
Wir schreiben dir, sobald eine neue geprüfte Stelle dazukommt. Höchstens eine Nachricht am Tag, und nur, wenn es tatsächlich etwas Neues gibt.
Abmelden mit einem Klick, jederzeit. Keine Weitergabe an Dritte.
SpAItial is pioneering the next generation of World Models, pushing the boundaries of generative AI, computer vision, and the simulation of reality.
SpAItial is pioneering the next generation of World Models, pushing the boundaries of generative AI, computer vision, and simulation.
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Warum dieser Job gelistet ist
Das Arbeitsergebnis ist selbst KI: Ohne die KI-Aspekte (Diffusion, World Models, Modelltraining) gäbe es keine Kernaufgabe mehr.. Prüfverfahren ansehen
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.
Key Qualifications: - 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.
Interesse an dieser Position?
Die vollständige Anzeige und das Bewerbungsformular liegen beim Arbeitgeber.
Zur OriginalanzeigeModelle bauen, trainieren und verbessern: von klassischem Machine Learning über Computer Vision bis zum Feinschliff großer Sprachmodelle.