AI Scientist - Machine Learning for Spatio-Temporal Prediction (KR)

Job Description

We are seeking a highly motivated AI Scientist specializing in ML-based Spatio-Temporal Prediction to join our growing AI R&D team. In this role, you will be at the forefront of developing and deploying cutting-edge ML models to solve real-world tabular, time series, and multimodal modeling challenges in manufacturing. We’re looking for a candidate with strong practical R&D experience, grounded in solid theoretical fundamentals, and deep expertise in AI disciplines. The ideal candidate will have a deep understanding of state-of-the-art machine learning algorithms and techniques, a track record of impactful publications in top-tier conferences such as NeurIPS, ICML, ICLR, KDD, CVPR, or ICCV, and a solid background in computer science and engineering. Experience collaborating with software engineering teams to scale and productize ML solutions is a strong plus. This is a high-impact role that combines foundational research, system-level design, and hands-on implementation. You’ll work closely with cross-functional teams to develop innovative solutions that guide strategic decisions and deliver tangible business value.

Responsibilities

Develop and advance a Virtual Metrology (VM) algorithm and innovative prediction AI solutions using ML-based spatio-temporal prediction technology for semiconductor manufacturing processes

Increase VM prediction accuracy based on multimodal and multidimensional data, including tabular, time series, and images generated in manufacturing process

Build predictive and anomaly detection models based on diverse sensor and process data

Analyze data and develop models using statistical, machine learning, and deep learning approaches

Conduct feature engineering and variable optimization tailored to each manufacturing process

Evaluate and optimize model performance, hyperparameter tuning, ML/DL automation (MLOps), and continual improvements

Integrate model inference with manufacturing systems for both batch and real-time applications

Ensure explainable AI (XAI) techniques such as SHAP for model transparency and interpretability

Collaborate with process/quality/manufacturing engineers to design and optimize practical models for field deployment

Key Qualifications

Ph.D. or Master’s degree with 5+ years of experience in Computer Science, Machine Learning, Statistics, or a related field.

Proven experience in AI, machine learning, or deep learning development and application

Experience with tabular data, time series, multivariate data analysis, anomaly detection, and predictive modeling

Hands-on project experience with Python-based data analysis and machine learning (e.g., Scikit-learn, Pandas)

Proficiency in deep learning frameworks such as PyTorch

Familiarity with various AI algorithms such as regression, classification, decision trees, ensemble models (XGBoost, Random Forest), DNN, CNN, RNN, LSTM, Transformer, LLM, etc.

Experience in feature engineering, model automation, hyperparameter optimization, and data preprocessing

Strong publication track record in top-tier ML/AI conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV).