SpAItial is pioneering the development of a frontier 3D 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 3D content.
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. We value people who take immense pride in their work and place collective team goals above personal ambition. As a part of our startup, you’ll be at the forefront of the AI revolution in 3D technology, and we want you to be excited about shaping the future of this 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

• 1+ year experience working in industry on generative AI ML Engineering (preference for a similar fast paced company).
• University degree focusing on applied machine learning.
• Strong background in machine learning, deep learning, and large scale data.
• Strong programming skills in Python and familiarity with ML frameworks like PyTorch, or JAX.
• Industry experience with writing clean, well-documented, and reproducible code, and performing code reviews.

PREFERRED QUALIFICATIONS

• Experience with Diffusion models (image or video)
• Background in working with large-scale data and training runs .
• Experience with multi-modal LLMs (e.g., for image/video captioning)
• Experience in 3D geometry (Structure-from-Motion, SLAM, depth prediction, …).
• Contributions to open-source generative AI projects or relevant publications.
  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

• Develop and maintain Machine Learning pipelines, including training and inference infrastructure.
• Perform and analyze ML experiments, including generating appropriate visualizations for generative models.
• Optimize and refactor research code to improve memory and runtime efficiency and usability.
• Set up and manage cloud computing and GPU environments for large-scale experiments.
• Advocate for best practices in all stages of ML development.