Deep Learning Research Engineer at Loom.ai
San Francisco, California, United States
🇺🇸 (Posted Jun 2 2018)
About the company
Loom.ai is a VC-funded, YC-backed computer vision startup based in San Francisco. Founded by LucasFilm and DreamWorks alums, we are bringing visual identity into virtual conversations in messaging and VR.
Loom.ai is enabling a new era of virtual communication through the creation, animation and sharing of personalized, 3D avatars. Based in San Francisco, and an alumni of the Y Combinator Fellowship, the Academy Award-winning team has created a best-in-class solution powered by deep learning, computer vision and visual effects. Loom.ai recently announced its partnership with Samsung which brings its fully embedded SDK solution to power ‘AR Emoji’ on the brand-new Samsung Galaxy S9 and S9+ devices.
In just seconds, Loom.ai can take a single photograph and transform it into a fully representative, 3D avatar as personalized as the individual by recognizing the many nuances that make each face unique. Animatable and expressive in real-time, these avatars can be used to power current and future applications in mobile messaging, entertainment, AR/VR, e-commerce, video conferencing, broadcasting and more.
Our current team comprises multiple PhDs, has decades of experience writing industry-strength software for VFX/games along with a keen eye for scaling/infrastructure, very strong research creds (papers in SIGGRAPH/SCA/CVPR), and two Sci-Tech Oscars.
Skills & requirements
Experience applying deep learning to computer vision problems
Knowledge of convolutional networks and common architectures (Inception, ResNet, DenseNet, etc)
Proficiency with at least one deep learning library (TensorFlow, Torch, MXNet, etc)
Familiarity with traditional computer vision in C++ with libraries such as OpenCV
Solid software design skills and ability to write organized, efficient, readable and reusable research code
Strong communication and collaboration skills
MS/PhD in a related field with an emphasis on computer vision or machine learning, or BS with equivalent industry experience
Experience deploying machine learning models in production environments
Familiarity with distributed computing frameworks such as Hadoop or Spark, or distributed training of deep learning models
Knowledge of algorithms applied to faces such as face recognition, landmarking, or reconstruction, or advanced deep learning topics
Knowledge of algorithms for facial animation synthesis from audio, video and other signals
Publications in machine learning or computer vision conferences (CVPR, ICCV, ICML, NIPS, etc)
Ability and enthusiasm to learn new technologies quickly
Instructions how to apply
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