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    R&D Computer Vision Engineer, Autonomous Driving at TomTom (Amsterdam, Netherlands)
    TomTom Employer
    Amsterdam, Netherlands
    Job Type
    Job Location
    Full Time
    Amsterdam, Netherlands

    Job Description:

    Want to impact the future of map-making for autonomous driving:

    At TomTom, you are in a unique position to advance and to impact the future of autonomous driving. We are doing world-class research and development to innovate our map technology for self-driving cars, using the latest machine learning and computer vision techniques.Our mapping algorithms and models are from the cutting edge of deep learning — having some of the largest labelled maps-related databases in the world, we make use of both supervised and unsupervised learning algorithms, and apply them using a variety of deep networks for classification and segmentation. The latest SLAM and visual odometry algorithms help us to align separate data sources, and to represent them in a common coordinate frame.

    The Challenge

    • In order to create maps that are precise and detailed enough to allow for autonomous driving, we need to efficiently analyse incoming data from diverse on-car sensors such as LiDAR and cameras, yielding large amounts of image and video data. Our maps contain the precise location and a description of road elements such as paint markers, lane types, the road border, barriers, junctions, road connectivity, signage and more

    • Defining and being able to build a highly accurate map is essential for precise localisation and navigation of a self-driving car. As we need to be able to update and maintain our maps continuously, approaches to fully automate and scale this process are key in our research strategy

    What we expect from you

    • Strong academic background in computer vision and machine learning with a postgraduate degree, preferably a PhD

    • Proven practical experience with relevant topics in computer vision and machine learning

    • Experience with deep learning, and frameworks such as TensorFlow, Caffe2, or PyTorch

    • Close familiarity with the latest research on detection, segmentation, classification, tracking and SLAM

    • Preferably a strong publication record with the top-tier computer vision and machine learning conferences & journals

    • Expert knowledge of Python and modern C++

    • Top-notch programming skills and software engineering know-how, and a desire to keep learning

    • Ability to write clean, elegant and maintainable production-level code

    • Highly motivated to work in a research and prototyping environment, and to be getting your work into production

    • Creative problem solver and team player with strong conceptual skills

    • Ability to work well with others; TomTom is a collaborative environment

    • Active participation in our scientific reading group

    • Self-motivated and dedicated to excellence

    • Fluency in English

    What you can expect

    • Work together with professionals in an open, friendly environment where creativity is key

    • Your innovative solutions will be put to practice, changing people’s everyday lives

    • We are close to the research community and work on cutting edge algorithms and models

    • Take ownership of, and pride in what we do and achieve

    • Have access to huge amount of labelled maps-related databases

    • Mingle at events. You’re invited to our famous Summer Fest, Hackathons and Innovation Days

    • Get rewarded. From Performance to Patent Inventory bonuses, we have you covered

    • Settle in smoothly. Our team will make your move to Amsterdam a seamless experience

    Want to join us?

    We would like to meet you!

    We kindly ask you to complete the form and attachments in English.

    Pre-employment screening of all external candidates is part of the selection process


    Flexible Hours
    Letter of Recommendation
    Note : This project is an external project, and it was posted on the platform by the Gradbee Team. We curate all the internships available across the internet by visiting company websites, and social networks like Facebook, LinkedIn, WhatsApp, Twitter etc. If you are the owner of this internship / project and need to get it removed, kindly mail us at [email protected]