Thundersoft Vietnam

Summary:

The Data QC & Model QC Specialist is responsible for ensuring the quality of AI data and Computer Vision / Vision-Language models (VLM) before deploying into real-world products. This role plays a critical part in building a robust, accurate, and scalable data–model pipeline for AI and Robotics systems.

Job description:

· Ensure the quality of multimodal datasets (image, video, text, captions, instructions) used for VLM training and evaluation, working with internal teams and external data vendors.

· Define and enforce data quality standards for VLMs, covering visual accuracy, textual correctness, vision–language alignment, semantic consistency, and bias/noise detection.

· Review and audit data before and after annotation, identifying systemic issues such as misaligned captions, hallucinated descriptions, weak prompts, or inconsistent labeling.

· Design evaluation datasets and test protocols for VLM use cases including VQA, instruction following, multimodal reasoning, and human–robot interaction scenarios.

· Evaluate VLM performance using both quantitative metrics and qualitative behavior analysis, focusing on correctness, consistency, robustness, latency, and stability in real deployments.

· Perform error analysis and deliver clear recommendations for data improvement, prompt refinement, model retraining, and release (go/no-go) decisions.

Requirements

Relevant education and experience

· Minimum 3+ years of experience, strong analytical mindset with high attention to data quality and model behavior.

· Experience working with AI data pipelines, Computer Vision, VLMs, or multimodal datasets.

· Solid understanding of how modern VLMs work (vision encoder + language model, alignment, prompting).

· Familiarity with evaluating AI systems beyond raw metrics, focusing on semantic correctness and user-facing behavior.

· Experience working with large-scale datasets and structured evaluation workflows.

· Good communication skills to collaborate with AI engineers, researchers, and data vendors.

· Ability to read and understand technical documentation and research papers in English.

Preferred Qualifications

· Hands-on experience with annotation or review tools (e.g. Label Studio, CVAT, or custom review systems).

· Basic Python skills for data analysis or evaluation scripting.

· Understanding of MLOps / model lifecycle management.

· Experience working with external data vendors or crowdsourced annotation teams.

· Exposure to robotics, embodied AI, or human–robot interaction use cases

Personality/ Attitude

· Proactive, dedicated, business-oriented, responsible and willing to learn

· Good communication skills, creative problem-solving skills and attention to detail