Research Engineer

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. 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 3D 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

• Create data processing pipelines for large-scale training data for training foundation 3D models.
• Prototype, implement, and evaluate architectures, losses, and features to complex generative models.
• Design, monitor and analyze experiments of large training runs of foundation models.
• Building pipelines for data filtering, annotation, and curation.
• Building evaluation and visualization frameworks to provide insights on model performance.
• Close collaboration with researchers to provide data for training large models.

Key Qualifications

• University degree focusing on applied machine learning.
• Experience in modern 3D techniques (NeRFs, Gaussian Splatting) or image and video generative models (Stable Diffusion, VAEs, etc).
• Proficiency in Python and deep learning frameworks (PyTorch).
• Familiarity with cloud-based ML infrastructure (e.g., AWS, GCP, or Azure).
• Strong problem-solving skills and the ability to work independently in a fast-paced environment.

Preferred Qualifications

• Industry experience in ML.
• Deep understanding of generative modeling, including VAEs, GANs, and transformers.
• Experience in 3D geometry processing (Structure-from-Motion, SLAM, depth prediction, …).
• Background in working with large-scale data and training runs
• Experience with multi-modal LLMs (e.g., for image/video captioning)
• 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.
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