Machine Learning Engineer - Computer Vision

Redwood City, CA

Posted: 08/20/2019 Industry: Computer Vision Engineer Job Number: 23638
Category : Computer Vision Engineer
Location/City : CA - Redwood City
Area Code : 650
Job Type : 1: Full Time
Country/Locale :
Id : 23638


#23638 Machine Learning Engineer - Computer Vision
Location: Redwood City, CA

Company:

Our client enables cars and other robots to understand their surroundings in 3D and in real time using only passive sensors like cameras. Their technology returns far more information about the environment than active sensors like radar and lidar for a fraction of the cost. Their mission is to bring superhuman senses to existing vehicles and to enable mass production of fully autonomous systems.

We're looking for a few great engineers and scientists. You will be one of the first 10 people on the team.

Important to know

Our client has no non-technical managers. They already have revenue and recently raised a large Series A round led by a tier one VC. They're building out a new office in Redwood City (CA) not far from the Caltrain station. They do not have set working hours and are open to you working remotely. They're also willing & able to process visa transfers.


Responsibilities:

Lead our research into 3D scene understanding.

Oversee acquisition and labeling of a unique proprietary RGBD dataset.

Design and implement state-of-the-art networks for semantic segmentation, object detection, depth estimation and more.

Help to recruit and potentially lead a team of similarly-qualified engineers.


Experience:

Strong foundation in machine learning, statistics, and linear algebra.

MS or PhD in Computer Science or equivalent.

2+ years of academic or professional experience in deep learning as applied to image or video analysis.

Fluent in Python, familiar with C++.

Deep knowledge of TensorFlow or PyTorch

Nice to have (but not required):

5+ years of professional software development experience with strong engineering practices.

Experience with self-supervised learning techniques.

Experience in optimizing NNs for constrained platforms (mobile or embedded).

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