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.

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  Design, develop, and optimize motion planning algorithms that handle complex, interactive traffic scenarios

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  Formulate and implement trajectory generation methods that balance safety, comfort, and efficiency under real-world driving conditions

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  Research and apply
  state-of-the-art
  planning methods, including optimization-based approaches, probabilistic decision-making, reinforcement learning, and imitation learning

• 
  Integrate
  perception
  , prediction, and mapping outputs into planning pipelines for robust decision-making

• 
  Ensure real-time performance of planning algorithms on automotive-grade embedded hardware

• 
  Contribute to the development of closed-loop validation pipelines, including simulation, software-in-the-loop, hardware-in-the-loop, and on-road vehicle testing

• 
  Collaborate with multidisciplinary teams (
  perception
  ,
  prediction,
  map generation,
  controls, systems) to integrate planning algorithms into the self-driving autonomy stack

• 
  Stay current with advances in motion planning, decision-making, and learning-based approaches for autonomous driving

• 
  Drive engineering excellence by writing clean, efficient, and well-tested code

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

• 
  Strong background in motion planning, trajectory optimization, and decision-making methods (e.g., A*, optimization-based planning, graph search
  etc.
  )

• 
  Experience with reinforcement learning, imitation learning, or deep learning approaches for planning and control

• 
  Proficiency
  in Python and C++, with familiarity in ML frameworks such as
  PyTorch
  or TensorFlow

• 
  Solid understanding of vehicle dynamics, kinematics, and control theory

• 
  Hands-on experience with real-time systems, performance optimization, and deployment on embedded automotive hardware

• 
  Skilled in simulation-based development and validation using tools such as CARLA
  or MATLAB/Simulink

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  Strong mathematical foundation in optimization, linear algebra, probability, and statistics

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

• 
  Nice to have: Prior experience in self-driving or ADAS development and f
  amiliarity
  with functional safety standards (ISO 26262, SOTIF)