The next breakthrough in generative AI is to digitally synthesize dynamic 3D worlds. 3D world models enable us to create new content for games, adaptive digital twins of environments and synthetic worlds for training robots. But today’s 3D simulators and workflows are too hard to build and scale. Common Sense Machine’s mission is to build an AI that learns to autonomously translate multimodal inputs (images, text) into game-ready 3D worlds.

CSM is looking for an experienced deep learning engineer, with a track record of scaling large scale neural networks on heterogeneous datasets. You will have the opportunity to be at the cutting edge of 3D representation learning and deploy state of the art models to production workflows. 

Responsibilities:

• Establish the scaling laws for 3D world models

• Continually optimize and refine models for enhanced performance

• Handle large-scale 3D dataset pipelines for preprocessing and training

Requirements:

• Strong grasp of deep learning, especially diffusion models

• Proficiency in Pytorch

• Experience with 3D data processing and visualization

• Excellent communication and collaboration abilities

Preferred Qualifications:

• Experience with large-scale ML deployments

• Familiarity with cloud computing platforms