Senior Computer Vision Engineer

Аналитика рынка

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Описание

In this role at Dexterity, you will be working on writing Computer Vision code and building Machine Learning models for computer vision. The role will involve understanding our current systems and their limitations and bringing your expertise in Computer Vision and developing ML models. Additionally, you will be responsible for updating and scaling our current ML pipelines to cover more scenarios and improve accuracy. You will leverage your expertise around the subfields of semantic segmentation and instance segmentation, keep up to date with the latest developments in these areas and implement them using platforms like Pytorch, Tensorflow etc... Experience with RGBD datasets and point clouds is very important to the type of problems you will encounter in this role and experience in the areas of ML and CV for autonomous vehicles or robotics is very useful. In addition, a solid grounding in writing optimized code in C++ and Python. Additionally, familiarity with cloud infrastructure to build training and serving pipelines is also a key aspect to this role.  At Dexterity, our use cases require low latency inference that approaches real time and therefore you must be able to build highly performant models and serving architectures. While an understanding of open source solutions is a useful prerequisite, we also require that the candidate has experience building their own models and setting up training pipelines. The end to end process of gathering, augmenting, labeling data and curating datasets, studying performance and optimizing quality of the model, as well as training, deploying and troubleshooting models on the field will be under your purview. About Dexterity At Dexterity, we believe robots can positively transform the world. Our breakthrough technology frees people to do the creative, inspiring, problem-solving jobs that humans do best by enabling robots to handle repetitive and physically difficult work.  We’re starting with warehouse automation, where th

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