Brain Mind
Robotics
BioComputing


Product Market
Consumers
Stage 1:
The Infant Biocomputer
Stage 2: Stem Cells
Grafting of Neural Networks
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Employment
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Administration

Machine Learning
Engineers
Speech Learning
Engineers
Vision Learning
Engineers
Robotics
Movement Engineers
Stem Cell
Computing

Brain Mind Robotics
BioComputing

"Silicon Valley" (Palo Alto - San Jose) California


We Are Building a Robotic Bio-Computer, Based on the Functional Neuroanatomy of the Human Brain, and Which Can Speak, Reason, Read, Understand Language, Experience Self-Consciousness and Human Emotions, Think Creatively, and Physically Interact with the Environment.

We Are Hiring Engineers With Experience In Computer Science, Machine Learning, Robotics, Artificial Intelligence, and the Creation of Auditory and Visual Platforms For Speech, Object, and Face Recognition.

STAGE 1: The creation of a stand-alone unit which is programmed to "reflexively" respond to simple visual and auditory stimuli, and to "reflexively" move the "eyes", turn the "head", open and close the "mouth" and its "hands", and make sucking, chewing, swallowing, swimming, and leg-lifting stepping movements, and to raise its "arms" and touch its "mouth" and "face."

Machine Learning Engineers--Robotics--Qualifications

Experience in Natural Language Understanding, Computer Vision, Machine Learning, Algorithmic Foundations of Optimization, Data Mining or Machine and Artificial Intelligence.
Programming experience in one or more of the following: C, C++, scala, R, Python, Objective-C, Swift.
Experience in developing and deploying high-quality performance code into dev, test, QA/QC, and prod environments.
Experience on developing production level code on one or more of the following areas: statistical modeling, machine learning algorithms, data pipelines.
An understanding of machine learning pipelines, neural networks, survival analysis, cluster analysis, forecasting, anomaly detection, association rules, cognitive computing, artificial intelligence, etc.
Hands-on knowledge of software engineering practices and principles.
Experience in building production level machine learning pipelines using open-source technologies (hadoop, spark, hive, kafka, storm).
Ability to Prototype simple machine learning pipelines to quickly decide if an idea is promising or not.
Experience using machine learning techniques for classification, parsing, and/or ranking.
Experience in extracting signal from noise in large unstructured datasets a plus.
Experience in iOS development, AI deep learning and advanced machine learning technologies, and its application to Robotics, IoT, IIOT
Ability to Develop large-scale machine learning, deep learning platform, and frameworks. Optimize AI platform performance, algorithms to enable key AI solutions, projects and products.
Ability to Evaluate, modify and maintain forks of open source deep learning frameworks, such as one of the followings, TensorFlow, Caffe, Cuda-Convnet, PaddlePaddle.
Ability to Develop realistic AI/machine learning solutions.
Proficiency in AI assisted data mining, social mining, information classification, Knowledge graph etc.
Proficiency in AI assisted decision-making, optimization for robotics.


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