Embedded AI Software Engineer (Senior/Expert)

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 looking for an Embedded AI Inference Engineer to develop, deploy, and optimize AI models on embedded SoCs and hardware accelerators.

You will work across the complete inference pipeline — from AI model and computational graph to runtime, hardware accelerator, memory, and embedded application — with a strong focus on achieving product targets for latency, throughput, memory, power, and accuracy.

• Deploy CNN/DNN and vision models onto embedded AI platforms using runtimes such as Qualcomm QNN, TensorRT, TIDL, ONNX Runtime, or equivalent.
  
• Convert and optimize models from PyTorch/TensorFlow → ONNX → target inference runtime.
  
• Optimize models using FP16, INT8, quantization, PTQ/QAT, operator fusion, and graph optimization.
  
• Analyze operator compatibility, graph partitioning, accelerator mapping, and CPU fallback.
  
• Optimize AI workloads across heterogeneous CPU/GPU/DSP/NPU architectures.
  
• Profile and optimize latency, FPS, throughput, CPU/accelerator utilization, memory footprint, and memory bandwidth.
  
• Analyze and reduce data-movement overhead through DMA, shared memory, zero-copy, buffer management, and efficient memory pipelines.
  
• Optimize end-to-end pipelines such as:
  
  Camera → Pre-processing → AI Inference → Post-processing → Application
  
• Identify and resolve performance bottlenecks related to compute, memory bandwidth, synchronization, scheduling, and hardware utilization.
  
• Integrate and debug AI inference pipelines in embedded Linux environments using C/C++ and Python.
  

• Bachelor's or Master's degree in Computer Science, Computer Engineering, Electronics, Embedded Systems, AI, or related fields.
  
• Strong programming skills in C/C++ and working knowledge of Python.
  
• Good understanding of CNN/DNN, tensors, computational graphs, neural-network operators, and AI inference.
  
• Hands-on experience with ONNX and at least one embedded AI inference runtime or accelerator SDK.
  
• Understanding of FP32/FP16/INT8, quantization, PTQ/QAT, and model optimization.
  
• Understanding of heterogeneous computing using CPU, GPU, DSP, NPU, or dedicated AI accelerators.
  
• Good knowledge of embedded system concepts including multithreading, synchronization, memory management, shared memory, and IPC.
  
• Experience with profiling, performance analysis, and bottleneck identification.
  

Experience in one or more of the following is highly desirable:

• Qualcomm QNN / Hexagon DSP / HTP
  
• NVIDIA CUDA / TensorRT / Jetson
  
• TI TIDL / C7x / MMA
  
• Camera and computer-vision pipelines
  
• OpenCV, OpenVX, GStreamer
  
• DMA and zero-copy architectures
  
• DDR/cache/memory-bandwidth optimization
  
• Real-time or high-performance embedded systems
  
• Automotive ADAS, robotics, or edge-AI products
  

We are looking for an engineer who can go beyond simply “making the model run.”

The ideal candidate can understand and optimize the complete path:

Model → Graph → Runtime → Hardware Accelerator → Memory → Embedded Software → End-to-End Performance

and systematically determine why an AI workload is slow, where the bottleneck is, and how to optimize it for production embedded systems.