Expert Machine Learning Optimization 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.

Responsibilities

• 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

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

Benefits

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

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