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.

• Collaborate with perception, prediction, planning and other teams to optimize models for deployment on embedded automotive processors (e.g., NVIDIA, Qualcomm, TI)
  

• Profile and analyze CPU/GPU/accelerator performance to identify bottlenecks in runtime, memory usage, and throughput
  

• Implement and integrate model optimization techniques including quantization, pruning, distillation, mixed-precision training, and efficient neural architectures
  

• Build and optimize inference pipelines using TensorRT, ONNX, CUDA kernels, and other compilation frameworks to achieve low-latency, real-time performance
  

• Develop distributed training and scalable optimization pipelines to support large-scale training of self-driving models
  

• Benchmark and validate optimized models across diverse real-world and simulated driving scenarios, ensuring robustness under edge cases
  

• Collaborate with system teams to integrate optimized models into the full self-driving stack
  

• Contribute to CI/CD, build systems, and automated testing pipelines for deployment of optimized ML models
  

• Ensure compliance with automotive-grade safety, reliability, and performance 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, deep learning, and computer vision
  

• Proficiency in Python and C++
  

• Hands-on experience with PyTorch or TensorFlow, including large-scale model training and optimization
  

• Expertise in deploying and optimizing models for embedded or real-time automotive systems using CUDA, TensorRT, ONNX, or equivalent frameworks
  

• Skilled in profiling and debugging ML code on GPUs and accelerators using profiler tools such as NVIDIA Nsight or similar
  

• Strong understanding of memory management, parallelization, and performance optimization on embedded processors
  

• Experience applying software engineering best practices (CI/CD, testing, version control) to ML pipelines
  

• Excellent problem-solving skills and the ability to thrive in a fast-paced, collaborative team environment
  

• Nice to have: Prior experience in autonomous driving or ADAS development
  
  
  
  
  

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