ABOUT VINDYNAMICS:
At VinDynamics, we are building a global robotics and AI infrastructure platform — combining humanoid robotics, embodied AI, and large-scale data ecosystems to power the next generation of intelligent machines.
Backed by Vingroup, Vietnam’s leading technology conglomerate, VinDynamics is on a mission to accelerate the adoption of robotics worldwide through advanced AI, scalable platforms, and real-world deployment at global scale.
Our vision is to make robots more accessible, intelligent, and commercially scalable — enabling safer, more productive, and more connected lives across industries and everyday environments.
JOB DESCRIPTION:
- Design & Build AI Pipelines: Architect and optimize robust AI pipelines for automated data pre-labeling and classification across multi-modal datasets.
- Implement SOTA Models: Research, deploy, and fine-tune State-of-the-Art models for specific data types:
- Computer Vision: Utilize SAM (Segment Anything Model), YOLO, VLM, or Zero-shot learning models for object detection and segmentation in robot camera/video feeds.
- NLP/Text: Leverage LLMs (GPT, Llama, etc.) for intent classification and entity extraction from robot data/video.
- Develop Active Learning Mechanisms: Design intelligent systems where AI automatically identifies "edge cases" or "low-confidence" data samples, routing them to human annotators to optimize the efficiency of the manual labeling team.
- Multi-sensor Data Fusion: Collaborate closely with the Data Processing team to fuse and synchronize data from multiple sources (LiDAR, Camera, IMU) and apply unified labeling in 3D space.
- AI Quality Assurance (AI QC): Develop algorithms to evaluate discrepancies between AI-generated labels and Ground Truth, continuously monitoring and refining models to maintain high accuracy.
- Tooling & MLOps: Package AI models into scalable APIs or internal tools, integrating them seamlessly into the company’s data management platform. Establish CI/CD practices for model deployment and updates.
REQUIREMENT:
Relevant education and experience:
- Education: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a closely related technical field.
- Core Experience: 4-5 years of proven experience as an AI Engineer, Machine Learning Engineer, or similar roles, with a strong focus on Computer Vision (Object Detection/Segmentation/VLM), NLP.
- Model Expertise: Hands-on experience in training, optimizing, and fine-tuning Foundation Models (e.g., SAM, CLIP, Whisper, or open-source LLMs).
- Pipeline Development: Demonstrated success in designing and implementing automated machine learning pipelines for data pre-labeling, classification, or active learning at a large scale.
Personality/Attitude:
- Robotics Domain: Previous experience developing AI pipelines for humanoid robotics, autonomous systems, or industrial applications.
- Multi-sensor & Spatial AI: Familiarity with fusing and processing multi-modal sensor data, including IMU metrics, pose estimation data, and large-scale egocentric video feeds.
- Advanced Programming: Strong proficiency in C++ alongside Python, with knowledge of model deployment on edge devices or robotic platforms.
- System Architecture: Experience with cloud computing platforms (AWS/GCP/Azure) and architecting highly scalable CI/CD pipelines for machine learning.
Personality/ Attitude
- Data-Centric Mindset: Deeply prioritizes data quality, consistency, and continuous iteration over purely focusing on algorithmic complexity.
- Problem Solver at Scale: Possesses strong analytical thinking to troubleshoot and optimize models for high-speed inference across massive datasets (Terabytes of data).
- Collaborative & Cross-functional: Eager to work seamlessly between Data Scientists (to align on training needs) and Data Operations teams (to analyze and resolve labeling bottlenecks).
- Leadership Spirit: Proactive in taking ownership of technical challenges and willing to mentor junior team members.
Working Tools:
- Programming Languages: Highly proficient in Python.
- Deep Learning Frameworks: Deep expertise in PyTorch or TensorFlow.
- MLOps & Version Control: Mastery of Git, Docker, and ML pipeline orchestration tools such as Apache Airflow, MLflow, or Kubeflow.
- Infrastructure: Comfortable working with high-performance GPU clusters and standard database management systems.
BENEFITS:
- Attractive income, competitive and commensurate with individual capabilities.
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13th-month salary, gifts on public holidays and special occasions, and performance-based bonuses.
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Meal allowance, annual company trips, health insurance, and exclusive benefits within the Group’s ecosystem.
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Clear career development opportunities aligned with the company’s growth, with access to training programs based on capability and job role.
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A dynamic and open working environment with diverse cultural and sports activities.
