AI Engineer - Foundation Models & Auto-Labeling for Autonomous 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.

We are seeking an AI Engineer specializing in Foundation Models and Computer Vision to develop scalable auto-labeling solutions for autonomous-driving data. You will leverage Vision Foundation Models (VFMs), Vision-Language Models (VLMs), and multimodal AI to automatically generate high-quality annotations from large-scale driving datasets, reducing manual labeling effort and accelerating perception-model development.

Key Responsibilities

• Develop foundation-model-based auto-labeling pipelines for 2D/3D detection, segmentation, tracking, lanes, freespace, traffic signs/lights, and scene understanding.

• Adapt and fine-tune VFMs/VLMs and multimodal models for autonomous-driving domains using fine-tuning, prompting, distillation, and pseudo-labeling.

• Leverage temporal and multimodal information to improve label accuracy and consistency across driving sequences.

• Optimize foundation-model inference for large-scale, cost-efficient GPU processing and production deployment.

• Collaborate with perception, data, infrastructure, and annotation teams to integrate auto-labeling into the autonomous-driving data pipeline.

Requirements

• BS/MS/PhD in Computer Science, AI, Robotics, Electrical Engineering, or related field.

• At least 3 years of experience in similar positions.
• Strong expertise in Computer Vision, Deep Learning, and Foundation Models.

• Experience with Vision Transformers, VLMs, multimodal models, or self-supervised learning.

• Solid knowledge of object detection, segmentation, tracking, and 2D/3D perception.

• Strong experience with Python, PyTorch, and GPU-based model training/inference.

• Experience with large-scale datasets, model evaluation, and ML pipelines.

Preferred: Experience with autonomous driving/ADAS, SAM-family models, DINO/Grounding DINO, CLIP/SigLIP-style models, open-vocabulary perception, BEV/3D perception, active learning, model distillation, TensorRT/ONNX, or distributed GPU inference.

Benefits

• Opportunity to work on cutting-edge perception systems with real-world impact.