Software Engineer (Computer Vision)

Palo Alto
Machine Perception is a stealthy early-stage YC/a16z company founded by Stanford alumni with a mission to secure the planet through human-level visual intelligence. We're using cutting-edge deep learning to solve an incredibly important real-world problem. Our investors include YC (W17), Andreessen Horowitz (a16z), Stanford StartX, SV Angel, among others. We’re working with Fortune 100 companies and solving some of the most challenging computer vision problems. The founding team has expertise in deep learning, robotics and neuroscience, and previously shipped products at Google, Dropbox, Microsoft and Apple.


As a Computer Vision Software Engineer on the Machine Perception team, you will take ownership of Ambient's deep learning infrastructure and computer vision pipelines. You’ll solve challenging research problems and realize novel ideas into products. The ideal candidate for this role would be fluent and up-to-date with Computer Vision research and obsesses about high-quality software engineering to realize research ideas.


    • Implementing and training deep neural networks to solve a variety of computer vision problems, such as object detection, semantic scene segmentation, human pose estimation etc. 
    • Pushing the state of the art on standard computer vision tasks with massive proprietary video data. 
    • Building and maintaining the infrastructure for training and deploying models, including massive data pipelines, experiment management platform, visualization tools etc. 
    • Optimizing runtime efficiency of models for deployment. 
    • Complete ownership of data assets and annotation efforts.


    • BS / MS / PhD in Computer Science / Mathematics or related field with 2+ years of deep learning experience (computer vision). 
    • 3-5 years of software engineering experience in an academic or industrial setting. 
    • Holistic understanding of deep learning concepts, state of the art in computer vision research and the mathematics of machine learning. 
    • Experience training a variety of popular deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) etc.
    • Proficiency in at least one of the popular computational and deep learning frameworks, such as TensorFlow, Caffe, Theano etc. 
    • Proficiency in C/C++ and Python. 
    • Proven track record of high-quality engineering output (side projects, internships, research projects, full-time jobs etc.). 
    • The ability and the desire to work in the dynamic environment of an early-stage company.
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