Research Engineer - Productionization
- 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.
- University degree focusing on applied machine learning.
- Experience in modern 3D techniques (NeRFs, Gaussian Splatting) or image and video generative models (Stable Diffusion, VAEs, etc).
- Proficiency in Python and deep learning frameworks (PyTorch).
- 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.
- Industry experience in ML.
- Deep understanding of generative modeling, including VAEs, GANs, and transformers.
- Experience in 3D geometry processing (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.
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