Research Engineer
Description For Research Engineer
SpAItial is at the forefront of developing cutting-edge 3D foundation models, combining AI, computer vision, and spatial computing to revolutionize industries including robotics, AR/VR, gaming, and movies. As a Research Engineer, you'll be instrumental in building and optimizing data processing pipelines for large-scale training data, implementing complex generative models, and working with state-of-the-art 3D techniques.
The role offers a unique opportunity to work on pioneering technology in a fast-paced startup environment. You'll be part of a team pushing the boundaries of what's possible in 3D AI technology, working with modern techniques like NeRFs, Gaussian Splatting, and generative models. The position requires a strong background in machine learning, with hands-on experience in Python and deep learning frameworks.
This is an ideal role for someone who combines technical expertise with innovative thinking and a passion for advancing the field of 3D AI. You'll be working in either London or Munich, collaborating with researchers and fellow engineers to solve complex problems in 3D content generation and interaction. The company values creativity, technical excellence, and team collaboration, making it perfect for those who want to make a significant impact in the evolving landscape of AI and spatial computing.
The position offers the excitement of working on frontier technology while being part of a diverse and inclusive workplace that welcomes people from all backgrounds. If you're passionate about 3D technology, machine learning, and want to be at the forefront of the AI revolution in spatial computing, this role presents an exceptional opportunity to shape the future of how we interact with 3D content.
Responsibilities For Research Engineer
- 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
Requirements For Research Engineer
- University degree focusing on applied machine learning
- Experience in modern 3D techniques (NeRFs, Gaussian Splatting) or image and video generative models
- Proficiency in Python and deep learning frameworks (PyTorch)
- Familiarity with cloud-based ML infrastructure (AWS, GCP, or Azure)
- Strong problem-solving skills and ability to work independently in a fast-paced environment
- Industry experience in ML (preferred)
- Deep understanding of generative modeling, including VAEs, GANs, and transformers (preferred)
- Experience in 3D geometry processing (preferred)
- Background in working with large-scale data and training runs (preferred)
- Experience with multi-modal LLMs (preferred)
- Contributions to open-source generative AI projects or relevant publications (preferred)
