Research Engineer - 3D World Models

SpAItial is pioneering the next generation of World Models, pushing the boundaries of generative AI, computer vision, and the simulation of reality. We are moving beyond 2D pixels to build models that natively understand the physics and geometry of our world. Our mission is to redefine how industries, from robotics and AR/VR to gaming and cinema, generate and interact with physically-grounded 3D environments.

We're looking for bold, innovative individuals driven by a passion for pushing the boundaries of generative 3D AI. You should thrive in an environment where creativity meets technical challenge and be fearless in tackling the hardest problems in 3D world modeling. 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 SpAItial, you'll be at the forefront of building World Models that bridge generative AI and the physical world. 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.

We're seeking a Research Engineer to develop cutting-edge generative methods that create physically-grounded 3D environments. You will work on building, training, evaluating, and optimizing models that generate high-quality 3D content from images, video, and other inputs—with a focus on world-scale scenes that understand geometry, physics, and spatial consistency. This role is ideal for early-career engineers who have strong fundamentals in machine learning and 3D data processing, are passionate about generative models, and want to help define the next generation of World Model systems.

Responsibilities

- Design and develop cutting-edge generative 3D machine learning methods for creating high-quality 3D content from images, video, and other inputs.

- Build, train, optimize, evaluate models for 3D reconstruction, novel view synthesis, and world generation.

- Implement and experiment with state-of-the-art 3D representations including point clouds, meshes, and 3D Gaussian Splatting.

- Develop training pipelines and loss functions that improve geometry accuracy, visual fidelity, and consistency.

- Collaborate with researchers to integrate physics-aware priors and world model capabilities into generative systems.

- Analyze model performance, debug failure cases, and iterate rapidly to improve quality and robustness.

Key Qualifications

- Bachelor's or Master's degree, or equivalent project/research experience, in computer science, machine learning, computer vision, graphics, robotics, or a related field.

- Strong fundamentals in deep learning and generative models, in particular diffusion models and transformers.

- Solid understanding of 3D processing concepts such as camera geometry, depth, reconstruction, point clouds, meshes, or Gaussian splats.

- Proficiency in Python and deep learning frameworks such as PyTorch, with experience in model training and optimization.

- Ability to implement research papers, run experiments, and iterate quickly on new ideas.

- Strong coding skills and passion for building reliable, scalable ML systems.