Computer Vision Engineer - Applied AI
Our team within Uber is building Uber’s computer vision platform to democratize the technology at Uber. We are rapidly growing, and are responsible for developing state of the art computer vision and machine learning solutions to derive inferences from image/video data on our platform that directly drive efficiencies across the company. In doing so, we are aiming to revolutionize transportation. We believe it is important to be able to adapt easily to meet the fast pace of a rapidly evolving research, development, and testing environments. Have a good balance of theoretical knowledge and programming expertise. Thrive on applying their knowledge, learning new technologies and don't believe in one-size-fits-all solutions. Strive to prove that speed and quality are not conflicting - i.e. that both can be achieved at the same time.
3-years full-time Software Engineering work experience
Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning)
Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib
Familiarity with deep learning frameworks such as Tensorflow, Keras, Pytorch.
Ability to productionize and/or machine learning solutions in context of other machine learning systems and the larger distributed systems environment.
Python
Java
C++
Go
Tensorflow
Keras
C Programming
Pytorch
Data structures and algorithms
Great time management
Process Improvement
Prioritizing skills
Self-Driven
Communication Skills
Problem-solving and resourcefulness
Strong organizational skills
Attention to detail and accuracy
According to JobzMall, the average salary range for a Computer Vision Engineer - Applied AI in 1455 Market St, San Francisco, CA 94103, USA is between $111,000 and $147,000 per year. The range depends on job experience, education, and skills.
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Uber Technologies, Inc. is an American multinational transportation network company offering services that include peer-to-peer ridesharing, ride service hailing, food delivery, and a bicycle-sharing system. The company is based in San Francisco and has operations in over 785 metropolitan areas worldwide.
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