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Deep Learning/Computer Vision engineer

tmanthey – Posted on July 8, 2017 at 11:48 am –

  • Job Type: Full-Time
  • Employer: Mapscape B.V.
  • Location: Eindhoven
  • Link: http://www.mapscape.eu
  • Job Descriptions:

Our Mission

Navinfo is the largest
Digital Map Provider in China and third largest in the world.
As part of the Navinfo family our team at Mapscape supports our business
units to create maps for autonomous driving as well as classic navigation
maps. Our mission is to deliver innovative technology to further increase the
degree of process automation.
Our core competence is the extraction of map related features from images by
intense use of Deep Learning and Computer Vision. Part of our mission is to evaluate
latest technology for use in map automation and support business units with
the integration of this technology into a production environment.

Your responsibility

Your responsibility is
the design, implementation and maintenance of Deep Learning and Computer
Vision based software. This includes the design, training and release of Deep
Neural Networks and related tools to our customers in China.

You will join our multi-cultural team of Deep
Learning/Computer Vision engineers within the ‘Innovation and Development’
(I&D) department.

While most of the team is located in our
Eindhoven office, our team is partially distributed across Europe. Our
processes support remote work, yet our preference is to extend our team in
Eindhoven. Keeping our team’s motivation on highest level is a core concern to
us. Therefore we allow a certain degree of individual flexibility to support a sustainable
work/life balance.

Work Environment

You will work with up to date Deep Learning
technology in a team of highly skilled professionals. Being a team player with
excellent communication skills is a key requirement for us. As English is our working
language your level should be B2 or higher.

As Deep Learning is a highly dynamic field we
continuously try to follow the latest technology developments. Our aim is to
pick up latest technology trends quickly and evaluate it for our mission.

To develop best in class deep neural networks for
our customers, part of our mission is the implementation of tools that support
the training and data management process. We aim at a ratio of 70:30 for Deep
Learning to data processing tools development.

As our tools are used in an industry production
process our software is currently mainly developed in C++ (11) on Windows
(Visual Studio). We also support Linux on PC and NVidia Jetson embedded

We support use cases
from Deep Learning object detection in a real-time in-car scenario to server
based Deep Learning Scene Segmentation. See our YouTube channel
https://www.youtube.com/channel/UCBTCXlxVBcC6c5RArXfgTwA to see some results
we published.

Core Tasks

•    Design of deep neural networks to tailor fit our customer requirement
•    Follow technology trends in Computer Vision/Deep Learning domain
•    Evaluate latest technology for use in the map automation process
•    Develop prototypes that demonstrate the potential for map automation
•    Support our partnering business units with the integration of our prototypes into a production environment
•    Develop tools to improve our Deep Learning related processes
•    Maintain clear and effective communication with the team and other team as necessary


•    A MSc or BSc degree, preferably in machine learning, computer vision, computer science, mathematics or physics with minimum 3 years work experience
•    Excellent knowledge of C++
•    Good knowledge in OpenCv
•    Good knowledge of Deep Learning
•    Good knowledge in the areas of object detection, object segmentation and machine learning
•    Good knowledge of Linux, Windows, Visual Studio, CMake
•    High commitment to quality
•    English level B2 or higher
Command of written and spoken Chinese nice to have

EU-Working Visa

We offer EU-Working Visa for non-EU professionals. We support you with relocation to the Eindhoven. After 5-7 years Dutch EU nationality and passport can be applied.

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