SpAItial is pioneering the development of a frontier image/video foundation model, pushing the boundaries of AI, computer vision, and spatial computing. Our mission is to redefine how industries, from robotics and AR/VR to gaming and movies, generate and interact with image/video generation.
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 our startup, 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.

KEY QUALIFICATIONS:

• PhD in Computer Science or related field with a strong publication record in Machine Learning, Computer Vision, Robotics, or Graphics.
• Strong knowledge of generative models (image/video diffusion).
• Strong knowledge of modern transformer architectures

PREFERRED QUALIFICATIONS:

• Experience in generative models (GANs, diffusion, LLMs, etc).
• Rich experience with large-scale deep-learning in PyTorch or similar.
• Experience with modern video/image generative diffusion models.
• Strong coding skills, able to rapidly iterate through ML experiments.
  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

• Design and develop image/video diffusion foundational ML models.
• Large-scale distributed model training.
• Develop demos showcasing the trained model prototypes.
• Processing and maintaining large data for model training.
• Productionizing models, test-time model optimization.