ML Research Engineer

at  SpAItial

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.

KEY QUALIFICATIONS

• University degree focusing on applied machine learning
• Strong background in machine learning, deep learning, and computer vision.
• Proficiency in Python and deep learning frameworks (PyTorch).
• Solid understanding of generative modeling, including VAEs, GANs, and transformers.
• Experience in optimizing large-scale deep learning models for compute efficiency.
• 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
• Experience with image and video diffusion models (e.g., Stable Diffusion, CogVideo, Mochi, …).
• Experience in 3D geometry (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

• Research, design, and implement cutting-edge deep models for image and video generation.
• Optimize & develop large-scale training data pipelines
• Optimize and scale deep learning architectures for efficient training and inference.
• Fine-tune existing large foundational models to output additional modalities.
• Optimize inference pipelines for deployment in product
• Collaborate with the 3D researchers on developing a 3D foundational model