IC Layout ML Intern — Verification & Data Insights

MediaTek; Singapore; On-site; SGD 20,000 - 29,000; Full time

Job summary

MediaTek’s CPV team is seeking students to apply their software skills to verify chip design using ML in the Physical Verification domain. You will work on ML systems to predict trends and detect anomalies in verification data, while collaborating with data engineers on ML pipelines and data preparation.

The role includes researching ML algorithms, running experiments, and training models as needed, with a focus on practical ML applications in semiconductor verification.

Qualifications

• Currently enrolled in a BSc/MSc from a reputable university.
• Proficiency in Python and libraries such as NumPy, pandas, and matplotlib.
• Excellent communication skills, self-motivated and well organized.
• Can-do attitude, eager to learn and curious.
• Experience with ML frameworks (Scikit-learn, PyTorch, TensorFlow, Keras) is a plus.
• Knowledge of SQL databases, MongoDB, and cloud infra (GCP) is a plus.
• Coursework related to IC design or semiconductor physics is a plus.

Responsibilities

• Design, develop and improve machine learning systems.
• Work closely with Data engineers on Data and ML pipelines.
• Study, transform, convert and select data sets.
• Visualize data for deeper insights.
• Research and implement appropriate ML algorithms and tools.
• Run machine learning tests and experiments.
• Train/retrain ML models and systems when necessary.

Skills

Python

NumPy

Pandas

Matplotlib

Scikit-learn

PyTorch

TensorFlow

Keras

SQL

MongoDB

GCP

ML Pipelines

Education

BSc/MSc in Computer Science or related

Job description

MediaTek’s CPV team is seeking students to apply their software skills to verify chip design using ML in the Physical Verification domain. You will work on ML systems to predict trends and detect anomalies in verification data, while collaborating with data engineers on ML pipelines and data preparation.

The role includes researching ML algorithms, running experiments, and training models as needed, with a focus on practical ML applications in semiconductor verification.