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Senior/Lead, Computer Vision Researcher/Engineer, SLAM

Tel Aviv Engineering

Job Description

The primary responsibility of the SLAM researcher/engineer is to conduct independent research and development on advanced perception components within the agreed-upon scope and schedule as defined by the management team. This individual must come with in-depth peer-recognized expert knowledge in at least one of the following core technical areas of geometric vision and machine learning: sensor geometry and calibration (cameras, IMUs), visual-inertial odometry, sparse and/or dense environment mapping, nonlinear optimization. They will participate in release planning, scheduling and actively participate as an individual contributor to the core technical development work.


Conduct independent research and development work on advanced computer vision and robotics components covering key product critical perception features such as visual-inertial odometry, SLAM, bundle adjustment, loop closure, sparse and dense environment mapping, sensor calibration
Provide leadership and mentorship to engineers within the computer vision group
Write maintainable, reusable code, leveraging test-driven principles to develop a high-quality geometric vision and machine learning modules


  • BSc/MSc in Computer Science or Electrical Engineering (with a minimum of 5 years of relevant experience)
  • 2+ years of working experience in Computer Vision targeted to product development
  • Expert knowledge in Computer Vision with experience in all of the following domains:
    • Sensor Calibration: Design and implement algorithms for online and offline intrinsic and extrinsic calibration of complex devices composed of several sensors, cameras, IMUs, depth sensors, and imagers. Collaborate with other engineers on the design and deployment of fully automatic robotics-aided calibration processes targeted for factory production
    • Visual-Inertial Odometry: Research and implement advanced algorithms for estimating the 6DOF pose of a head-mounted device by optimally fusing visual and inertial measurements collected from multiple cameras and IMUs
    • Large scale mapping: Design and implement advanced algorithms for large scale mapping to extend computer vision spaces to large areas
    • Dense Environment Mapping: Design and implement advanced algorithms for reconstructing dense 3D models of large-scale indoor environments using depth sensors
    • Computer vision algorithms on cloud: Research, architect, and implement high-performance computer vision software in the cloud with state-of-the-art capabilities
  • Expert level in C/C++  and Python (programming and debugging)
  • Experience working with G2E or GTSAM
  • Experience working with OpenCV
  • Experience in Deep Learning is preferred with knowledge of at least one of TensorFlow, PyTorch, or Caffe
  • Knowledge of parallel computing, OpenCL, CUDA, GPGPU, SIMD is an advantage
  • Knowledge software optimization and embedded programming is an advantage

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