Deep Learning Engineer

    Swarm Analytics Published: August 5, 2020
    Location
    Innsbruck, Austria
    Job Type

    Description

    Swarm Analytics develops revolutionary AI technology for transforming cameras into smart sensors. It is able to extract real-time information from video streams directly at their source with unparalleled efficiency.

    Role Description:

    You will be part of the engineering team. Together we build faster, smarter, and more affordable computer vision-based systems for the smart city. Among others, you will be involved in building production-grade training systems, model architectures, scalable training-environment in the cloud, and model optimizations.

    Minimum Requirements:

    • 2+ years of professional experience as a software engineer (shipped software to production)

    • 2+ years of experience with deep learning and/or computer vision (built models for production)

    Preferred Requirements:

    • Profound knowledge/experience with Pytorch/Tensorflow (or similar), CNN backbones, detection systems, inference engines, model compression, ONNX, hyperparameter-/network architecture search

    • Experience with scalable cloud environments, Multi GPU and distributed training, experience to train large data sets

    • Experience with UNIX based systems, test-driven development, docker, CI/CD

    Conditions:

    • Starting date as soon as possible

    • Permanent full-time contract (38,5h/week)

    • Flexible working time

    • An office in the heart of the Alps (okay, that's not our merit, but still great!)

    • An annual minimum wage starting from 55.000€ gross

     

    We look forward to receiving your application directly online via the form “Apply now” on our website: https://www.swarm-analytics.com/jobs/deep-learning-engineer

    You can find more information on our homepage and social media channels. If you have any questions, please read the FAQ on our careers page (https://www.swarm-analytics.com/career) or contact our People & Culture Manager Sarah Nobis ([email protected]).

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