Research Scientist Intern - Neural Rendering

“The most talked about AI company in Hollywood”

Flawless is shattering the boundaries of traditional filmmaking with its groundbreaking suite of Gen AI film editing tools. Our mission is to empower filmmakers with cutting-edge technology that allows creativity without compromise, expands storytelling possibilities, and delivers unparalleled visual and emotional experiences.

What we are looking for: As a research scientist intern on the neural rendering team at Flawless, you will work with a close-knit, passionate group of world-class researchers tackling some of the most challenging problems in deep learning including multi-modal image synthesis, audio-visual data fusion, 3D computer vision, and the application of some of the most exciting works in generative AI. 

Our work in automated visual translation is just the beginning, we’re developing countless exciting products based on the application of our proprietary, cornerstone research.  This is an unbelievable opportunity to join a team operating at the cutting edge of the generative revolution, don't hesitate, to reach out today.

Responsibilities:

• Participating in cutting-edge research in 3D-controlled dynamic face neural rendering.
• Solving real problems in enabling audio-driven photorealistic talking head synthesis.
• Developing efficient deep neural network models for photorealistic neural rendering.
• Collaborating with and supporting other researchers across various departments.
• Pushing the state-of-the-art research from top-tier conferences (e.g. CVPR, ICCV, ECCV, SIGGRAPH, etc.)

Requirements:

• On-going Ph.D. in Visual Computing, Computer Vision, Machine Learning, or a related field
• Proven track record of first-authored publications at leading conferences (e.g., SIGGRAPH, CVPR, ECCV, ICCV, NeurIPS, ICLR, ICML) or journals (e.g., PAMI, IJCV, JMLR, ToG)
• 2+ years experience with building systems based on Computer Graphics, Computer Vision, or Machine Learning methods.
• Strong experience with deep learning and computer vision in one or more of the following fields:
• Generative Neural Networks (Diffusion Models, GAN, VAE, etc. )
• Neural networks and transfer learning
• Un-supervised or self-supervised learning
• Few/zero-shot learning for image synthesis
• Neural talking head synthesis
• Domain adaptation techniques for image face synthesis
• 3D computer vision
• Proficiency with deep learning frameworks such as PyTorch.
• Proficiency in statistical methods and numerical optimization
• Interest in building real-world tools with product impact

Nice to Have:

• Experience with 3DMM and statistical face models
• Experience with Transformer, CNN, GAN, and Diffusion model architectures
• Experience with differentiable rendering pipelines, such as PyTorch3D
• Interpersonal experience: cross-group and cross-culture collaboration