Senior AI Engineer (Computer Vision & Data Pipeline)

About the Role 

We are looking for Senior AI Engineers to build the automatic labeling and data pipeline behind VinFast’s ADAS and autonomous driving programme. The team turns raw multi-sensor driving data into high-quality training data at scale, and closes the loop so that every human correction makes the next model better. 

This is a fullstack AI role. You will train and evaluate computer vision models, and you will also build the pipelines, tooling and integrations around them. We do not split the team into modellers and engineers: everyone is expected to take a problem from data through model to production pipeline, and to grow across the whole flow over time. Work is assigned according to what the programme needs at that moment, not by a fixed job boundary — so we are looking for people who want breadth, not a single lane. 

Tech stack: Python and PyTorch, Airflow for orchestration, Docker and Kubernetes, running on GPU clusters with object storage. 

What the Team Covers 

You will contribute across all of the following over time. We do not expect one person to master every part on day one, but we do expect you to be willing to work in any of them. 

• Computer vision models over camera and LiDAR data — detection, segmentation and tracking 
  

• Automatic pre-labeling with confidence scoring, and routing work between machine and human review 
  

• Automated data pipelines that run the full flow end to end at production volume 
  

• Integration with annotation tooling, and conversion of data into standard training and simulation formats 
  

• Mining the data archive for the scenarios and edge cases that matter most 
  

Key Responsibilities 

Models & Evaluation 

• Train, fine-tune and evaluate computer vision models on large multi-sensor datasets 
  

• Measure model quality with standard metrics, and drive targeted improvement from error analysis 
  

• Apply human-in-the-loop techniques: pre-labeling, confidence scoring, and feedback from human corrections 
  

Data & Pipelines 

• Build and operate automated pipelines that process data end to end at production volume 
  

• Prepare and manage datasets: cleaning, splitting, augmentation, versioning and format conversion 
  

• Ensure pipelines are reproducible, observable and recoverable — no silent failures 
  

Collaboration & Delivery 

• Work with the annotation team so that labeling standards match what the models actually need 
  

• Partner with the platform team to deploy models and pipelines into production 
  

• Document workflows and data contracts so other teams can use the output without asking 
  

• Strong Python and PyTorch (or TensorFlow) 
  

• Solid experience training and evaluating computer vision models in production, not only in research 
  

• Experience building automated data pipelines with Airflow or a similar orchestrator 
  

• Comfortable with Docker, Kubernetes and working on GPU clusters 
  

• Experience with large-scale datasets and with imperfect labels 
  

• Willingness to work across the full flow — model, pipeline and integration — rather than within a single specialty 
  

• Good communication in English — technical documentation and working with international partners 
  

Nice to Have 

• Experience with 3D data: LiDAR point clouds, 3D object detection, sensor fusion or multi-camera calibration 
  

• Experience with annotation tools such as CVAT or Label Studio, including customising or extending them 
  

• Familiarity with autonomous driving datasets (nuScenes, KITTI, Waymo) or simulation data formats 
  

• Experience with MLOps tooling — experiment tracking, model registry, inference serving 
  

• Contributions to open-source ML projects, or publications in computer vision