Expert Machine Learning Perception Engineer – Self-Driving

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 seeking a highly skilled Senior ML Perception Engineer to join our self-driving team. In this role, you will design, develop, and deploy advanced perception algorithms that enable autonomous vehicles to accurately sense, interpret, and interact with the world around them. You will work on cutting-edge challenges in 3D object and occupancy detection, tracking, sensor fusion, scene understanding, and foundation models for self-driving, with direct impact on the safety and performance of our autonomous systems. This is an opportunity to contribute at the intersection of deep learning, computer vision, robotics, and system engineering, while collaborating with a world-class team of researchers and engineers.

• Develop, prototype, and optimize algorithms for 3D object and occupancy detection, multi-object tracking, semantic segmentation, and 3D scene understanding using multi-sensor data (camera, LiDAR, radar, etc.)
  

• Design and adapt foundation models for self-driving perception, enabling scalable, generalizable representations across tasks and sensor modalities
  

• Build and improve multi-sensor fusion pipelines to enhance robustness under diverse and challenging driving scenarios
  

• Research, adapt, and implement state-of-the-art methods in machine learning and computer vision (e.g. BEV perception, occupancy networks, multimodal fusion, end-to-end models)
  

• Translate research insights into production-ready solutions, ensuring scalability, robustness, and efficiency
  

• Optimize deep learning models for real-time inference on automotive-grade hardware
  

• Design and execute evaluation, benchmarking, and validation of perception models across real-world datasets and simulation environments
  

• Collaborate closely with cross-functional teams to integrate perception models into the full self-driving stack
  

• Contribute to data curation, annotation strategies, and scalable training pipelines to accelerate perception development
  

• Drive engineering excellence by writing high-quality, efficient, and well-tested code
  
• Stay current with latest advances in computer vision, deep learning, robotics, and foundation models, and contribute to publications and patents when possible

Requirements

• MSc/PhD in Computer Science, Electrical Engineering, Robotics, or a related field, with 5+ years of relevant industry experience
  

• Strong foundation in machine learning, computer vision, and 3D geometry
  

• Hands-on experience with 3D object detection/tracking architectures
  

• Familiarity with foundation models for vision or multimodal learning (e.g., large-scale pretraining, transfer learning, self-supervised learning etc.)
  

• Proficiency in Python and/or C++, with expertise in modern ML frameworks (PyTorch, TensorFlow)
  
• Experience in handling 3D point cloud data
  
• Knowledge of multi-sensor calibration and fusion techniques
  

• Strong software engineering skills with emphasis on scalability, reliability, and performance optimization
  

• Ability to take algorithms from research to deployment in production systems
  

• Excellent problem-solving skills, creativity, and ability to work in fast-paced, collaborative environments
  

• Nice-to-Have:
  

• Experience with autonomous driving or ADAS perception systems
  

• Background in SLAM, 3D reconstruction, occupancy networks, or sensor simulation
  

• Track record of publications in top-tier conferences

Benefits

• Competitive salary
  
• Opportunity to collaborate with and learn from industry-leading professionals in the automotive domain.
  

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