NextEV is hiring a

Staff Engineer, Embedded Machine Learning Platform

San Jose, United States

As a NEXTEV Engineer in the Machine Learning team, you will have the extraordinary opportunity to apply leading edge research to revolutionize the automotive domain.
You will utilize your expertise in GPU computing and data processing to create a platform for machine learning applications.  This platform will integrate with cloud systems and run embedded algorithms that will ultimately provide greater comfort, convenience, personalization, and safety for both vehicle occupants and the public at large.
Fully understand the capabilities of the embedded compute engine as related to machine learning applications.
Develop and maintain a machine learning platform and SDK to provide modelling, prediction, and integration capabilities.
Collect and prepare in-car data as required by embedded machine learning algorithms, and upload data to cloud systems as necessary.
Provide necessary tool chain for algorithm developers to create, test, and harden their algorithms.
Masters or PhD in computer science, computer engineering, applied mathematics or related field.
10+ years of combined experience in distributed computing, data analytics, and/or embedded computing.
Expertise in GPU optimization via CUDA or OpenCL.
Proficiency in Python, C++, and Java.
Familiarity with modern software development tools for source code control, unit testing, automated build, test, and deployment.
A passionate, curious mind that is driven to find solutions to difficult problems. A capability to work either independently or collaboratively as projects evolve.
Desirable Attributes:
Experience with deep learning frameworks such as Caffe, Torch, Theano, or TensorFlow.
Experience with computer vision toolkits, especially OpenCV.
Experience with collection and preparation of visual datasets for training.
Experience with large scale training using cloud services.
Experience with embedded Linux systems, and/or mobile platforms (Android, iOS).
Experience with the automotive domain.