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

• Strong Python coding skills for scripting and data processing on the cloud.
• Ability to efficiently gather and maintain terabytes of data for ML training using a data-processing cluster.
• Experience with 3D graphics and rendering.

PREFERRED QUALIFICATIONS

• Experience with handling 3D data (camera matrices and coordinate conventions, depth maps, point clouds, 3D Gaussian Splats).
• Knowledge of frameworks for handling large data (SQL, H5, Webdataset, Apache Superset).
• Experience with training (3D) deep models in PyTorch.
  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

• Gathering, processing, and maintenance of large-scale training data for training foundational 3D models.
• Building large distributed pipelines for rapid data filtering, annotation, and curation.
• Close collaboration with the machine learning team to provide data for training large models.
• Maintaining and utilizing a metadata lake for efficient data filtering, analytics, and visualisation.