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Personalized expressive 3D avatars from photographs for chat, mobile AR, VR, e-commerce

Deep Learning Research Engineer

$80k – $150k • 0.05% – 0.15%
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Loom.ai is a venture-backed tech company headquartered in San Francisco, CA. Loom.ai enables a new era of virtual communication by automatically converting selfies into expressive 3D avatars. The core technology leverages Loom.ai’s deep expertise in computer vision and machine learning combined with VFX magic to create life-like human face visualizations. The animatable and expressive faces can be used to power applications in messaging, live streaming, AR, VR, gaming, e-commerce and virtual classrooms for individuals and businesses around the world. Loom's API is currently in private beta and is being actively integrated into mobile platforms with millions of users. Our current team comprises multiple PhDs, has decades of experience writing industry-strength software for VFX/games/ML and Computer Vision, a keen eye for scaling/infrastructure, very strong research creds (papers in SIGGRAPH/SCA/CVPR), and three Sci-Tech Oscars. You have an opportunity to join us at a pre-series A stage and have a direct impact on shipping applications with millions of users.

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

Plus:

​- 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

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