VINFAST

Expert Map Generation Engineer - Self-Driving

VINFAST Frankfurt

Stellenbeschreibung:

Expert Map Generation Engineer - Self-Driving

Frankfurt am Main, Germany | Posted on 11/06/2025

VINFAST is a pioneering electric vehicle (EV) company committed to revolutionizing the automotive industry with sustainable and innovative mobility solutions. As a leading player in the EV market, VinFast is dedicated to delivering high-quality, cutting-edge electric vehicles that redefine the driving experience. Our team consists of passionate professionals driven by a shared vision of creating a greener and more sustainable future through innovation, technology, and excellence.

We are looking for a Senior Map Generation Engineer to advance our online and offline mapping and localization capabilities for self-driving vehicles. In this role, you will design, develop, and deploy algorithms that dynamically generate, update, and maintain high-quality maps directly from multi-sensor data. Your work will ensure our autonomous vehicles navigate safely and reliably using generated maps, enabling robust performance in dynamic, real-world environments.

  • Develop and optimize mapping algorithms that fuse data from LiDAR, radar, cameras, and GNSS/IMU to generate semantic, geometric, and topological maps
  • Collaborate with perception, prediction, and planning teams to integrate mapping outputs into the full autonomy stack
  • Research and apply state-of-the-art techniques in deep SLAM, neural implicit representation to support map generation
  • Design and implement large-scale map generation workflows, leveraging both real-world and simulated datasets
  • Optimize models and algorithms for deployment on automotive embedded platforms with real-time performance constraints
  • Work with validation and simulation teams to validate mapping and localization algorithms at scale
  • Drive engineering excellence by ensuring high-quality, modular, and tested code
  • Contribute to the research community through publications or open-source contributions in mapping and localization for autonomous driving

Requirements

  • MSc/ PhD in Computer Science, Robotics, Electrical Engineering, or a related technical field and 5+ years of industry experience
  • Strong background in localization, mapping, and SLAM (visual, LiDAR, or multi-sensor)
  • Experience with deep learning–based approaches for mapping and localization (e.g., neural SLAM, implicit representations, deep visual odometry)
  • Proficiency in Python and C++, with hands‑on experience in ML frameworks such as PyTorch or TensorFlow
  • Familiarity with 3D computer vision, geometric deep learning, and multi-sensor calibration
  • Familiarity with probabilistic state estimation (Kalman filters, particle filters , graph optimization )
  • Track record of bringing research concepts into production-ready solutions
  • Strong problem‑solving skills and ability to deliver robust algorithms under real‑world conditions
  • Nice to have: Experience in ADAS or autonomous driving localization/mapping systems
  • Opportunity to collaborate with and learn from industry-leading professionals in the automotive domain.

With respect to all your personal data shared to VinFast in the application and the entire recruitment process of VinFast, by clicking “Apply”, submitting your resumé/CV and/or participating in VinFast's recruitment process, you agree that you have read VinFast's Personal Data Protection Policy ("Policy") posted at or you agree to the Policy and consent for VinFast to process your personal data in accordance with the Policy and the applicable regulations on personal data protection.

To all recruitment agencies : VinFast does not accept agency resumes. Please do not forward resumes to our careers alias or other VinFast employees. VinFast is not responsible for any fees related to unsolicited resumes.

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Stelleninformationen

  • Veröffentlichungsdatum:

    11 Dez 2025
  • Standort:

    Frankfurt
  • Typ:

    Vollzeit
  • Arbeitsmodell:

    Vor Ort
  • Kategorie:

    Development & IT
  • Erfahrung:

    2+ years
  • Arbeitsverhältnis:

    Angestellt

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