Machine Learning / Deep Learning Engineer

Website loom_ai

Your solution for personalized AR avatars is a venture-backed tech company headquartered in San Francisco, CA with remote employees throughout the US, Berlin and Zurich. For this position, we’re hiring remote employees in Europe to grow our Deep Learning team. enables a new era of virtual communication by automatically converting selfies into expressive 3D avatars. The core technology leverages’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.

We’re working with a range of big scale partners, like Samsung, Qualcomm and Meitu, providing them with access to personal animated avatars.

Our current team comprises multiple PhDs, has decades of experience writing industry-strength software for VFX/games/ML and Computer Vision, very strong research creds (papers in SIGGRAPH/SCA/CVPR), and two 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.


– 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)
– Solid software design skills and ability to write organized, efficient, readable and reusable code
– Strong communication and collaboration skills
– Ability and enthusiasm to learn new technologies quickly
– MS/PhD in a related field with an emphasis on computer vision or machine learning, or equivalent industry experience


– Experience deploying machine learning models in production environments
– Familiarity with traditional computer vision in C++ with libraries such as OpenCV
– 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)

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