If you are a Applied Scientist (Image Restoration/Editing-Computer Vision) with experience, please read on!
Top Reasons to Work with Us
We are an exciting and rapidly growing startup operating at the intersection of AI and film making.
We have an exciting technology that uses generative AI to make our users look like their favorite actor or actress is speaking another language, without subtitles or voice-overs, making it indistinguishable from the original performance using automated visual translation.
We have been awarded Awarded TIME Best Inventions of the Year and also won Best Neural Network Award at AI Tech Awards!
What You Will Be Doing
As an applied scientist on the neural rendering and compositing team, 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, and application of some of the most exciting works in generative AI.
Solve challenging problems in deep learning including multi-modal image synthesis, audio-visual data fusion, and application of some of the most exciting works in generative AI.
Experience in Computational Photography, inpainting, coloring, blur, grain, camera/model optics
Image face synthesis, editing, restoration
Neural talking head synthesis
Unsupervised, Self Supervised learning, Zero/few shot learning for image synthesis, editing or restoration
Prefer experience in Experience with 3D GANs for human face synthesis/editing
· Experience with multi-modal image synthesis/editing and audio-visual data
· Implemented models from top-tier publications in venues such as Siggraph, ICCV, CVPR, ICML, or NeurIPS.
What You Need for this Position
Minimum Qualifications
MS or Ph.D. in Visual Computing, Computer Vision, Machine Learning, or related field
3+ years of proven industry or research experience with deep learning and computer vision in two or more of the following:
Computational Photography (mandatory) e.g. inpainting, blur, grain, color-space based modeling, camera models/optics
Domain adaptation techniques for image face synthesis/editing/restoration
Neural talking head synthesis
Un-supervised, self-supervised, few/zero-shot learning applied to image synthesis/editing/restoration problems
3+ years of experience in Python with proficiency in deep learning frameworks such as PyTorch or Tensorflow
Experience developing tools and solutions at scale for visual effects or real-world computer vision problems
Proficiency in signal processing and numerical optimization
Preferred Qualifications
Experience with 3D GANs for human face synthesis/editing
Experience with multi-modal image synthesis/editing and audio-visual data
Implemented models from top-tier publications in venues such as Siggraph, ICCV, CVPR, ICML, or NeurIPS.
-Proficiency in statistical methods and numerical optimization
What's In It for You
-Autonomy - You'll own your work from start to finish
-Influence - You'll impact major research decisions
-Publication - You'll be encouraged to publish work through collaborations with researchers
-Learning - Youll push the state-of-the-art with the best in the world
-Impact - Your input genuinely matters
-Workspace flexibility
-Stock Options
-Comprehensive medical, dental, and vision insurance
-401(k) plan
So, if you are a Applied Scientist (Image Restoration/Editing-Computer Vision) with experience, please apply today or apply directly to liana.pryor@cybercoders.com!
Applicants must be authorized to work in the U.S.