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., NVIDI
  A
  ,
  Qualcom
  m
  ,
  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