Perception and Localization Developer

Remote: 
Full Remote
Contract: 
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Offer summary

Qualifications:

Strong experience with multi-sensor data processing, including LiDAR, GPS, IMU, and 360° cameras., Proficiency in Python and/or C++ for data parsing and transformation., Understanding of SLAM, trajectory estimation, and sensor fusion concepts., Knowledge of coordinate systems, transformations, and registration techniques..

Key responsibilities:

  • Develop tools to decode raw LiDAR data into structured 3D point cloud formats.
  • Implement SLAM techniques to estimate platform trajectory and generate 3D maps.
  • Fuse GPS, IMU, and Doppler radar data for accurate trajectory production.
  • Align and synchronize data from multiple sensors for consistent spatial understanding.

EyeVi Technologies logo
EyeVi Technologies https://www.eyevi.tech
11 - 50 Employees
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Job description

EyeVi Technologies is a dynamic and rapidly growing technology company backed by venture capital. Our mission is to revolutionize road infrastructure management by providing on-demand mapping technology and AI-powered software. We are currently operating in 11 countries (in Europe and in US) and manage operations from - Estonia, Poland and Spain.


We are looking for a skilled Perception and Localization Developer to join our team working on multi-sensor perception, mapping, and localization systems. The developer will work with data from LiDAR, GPS, IMU, 360° cameras, and Doppler radar to build accurate environmental models and robust localization solutions.


Why EyeVi Technologies?

  • You will have the opportunity to work on cutting-edge technology with global impact and shape the future of our organization. Stock options available — join us pre-Series A and grow with the company.
  • Contribute to the development of an agile geoinformation platform with transformative potential. For the right candidate, we are also open to hourly contract arrangements.
  • Collaborative and inclusive work culture with opportunities for professional growth.


What will you be doing?

  • Develop tools to decode raw LiDAR data (e.g., PCAP or proprietary formats) into structured 3D point cloud formats (e.g., LAS, PCD). Perform point cloud alignment across multiple scans or timeframes to correct motion distortion and build coherent spatial representations.
  • Implement SLAM techniques to estimate the platform’s trajectory and generate 3D maps using LiDAR data, ensuring robustness across varying environments and sensor noise levels.
  • Fuse GPS, IMU, and Doppler radar data to produce accurate, smooth trajectories. Apply filtering, dead reckoning, and coordinate transformations to maintain localization during GPS outages or signal degradation.
  • Align and synchronize data from LiDAR, GPS, IMU, 360° cameras, and Doppler radar. Project between modalities for consistent spatial understanding and visual-ground truth correlation.
  • Align LiDAR point clouds with 360° camera imagery through geometric projection and spatial registration to enable multi-view visualizations and analysis.


You are a good fit, if you have:

  • a strong experience with multi-sensor data processing, including LiDAR, GPS, IMU, 360° cameras.
  • proficiency in Python and/or C++ for data parsing, filtering, and transformation.
  • an understanding of SLAM, trajectory estimation, and sensor fusion concepts.
  • an experience working with 360° camera systems and projection models.
  • knowledge of coordinate systems, transformations, and registration techniques.


Nice to have:

  • Experience with file formats like PCAP, LAS/LAZ, NMEA, and panoramic image standards.
  • Understanding of geodesy (e.g., WGS84, UTM, ECEF).
  • Background in computer vision, photogrammetry, or navigation systems.


PS! This is a remote role working for a company in Estonia.

📨 For more information, please reach out to our recruitment partner: helen@smartful.ee

Required profile

Experience

Spoken language(s):
English
Check out the description to know which languages are mandatory.

Other Skills

  • Collaboration
  • Problem Solving

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