Machine Learning Engineer

Advantest is seeking a motivated Junior Machine Learning Engineer to support the development of data-driven and ML-powered solutions for semiconductor R&D, test, and operations teams. In this role, you'll contribute to building predictive models, conducting statistical analyses, and assisting in the development of light-to-moderate data pipelines that help transform complex semiconductor datasets into actionable insights.


This position is ideal for a recent graduate with strong foundational ML skills who is eager to learn, collaborate, and grow in a fast-paced, technically rich environment. You'll work alongside experienced engineers, data scientists, and domain experts while gaining hands-on experience across the ML lifecycle-from data preparation to model deployment


Key Responsibilities

• Develop and evaluate ML models (e.g., classification, anomaly detection, regression, time-series analysis).

• Perform feature engineering and exploratory data analysis on semiconductor datasets.

• Contribute to model deployment workflows in collaboration with ML data scientists, following MLOps best practices.

• Assist in implementing model monitoring, retraining workflows, and documentation.
• Experiment with modern analytics techniques, including LLM-based or generative-AI methods, under guidance from senior team members.

• Help build and maintain ETL/ELT workflows that prepare data for analysis and modeling.

• Support data quality checks, versioning, and data validation tasks.

• Work with cloud and on-prem tools to help ensure data accessibility for ML applications.

• Work with semiconductor engineers and data scientists to translate domain challenges into analytical tasks.

• Support the creation of dashboards, reports, and visualizations that communicate insights clearly.

• Learn and apply semiconductor-specific data concepts with the support of senior mentors.

• B.S. or M.S. in Computer Science, Data Science, Engineering, Applied Math, or a related quantitative field.

• Hands-on experience with ML modeling via coursework, internships, or independent projects.

• Exposure to data engineering concepts-coursework or project-based experience is acceptable.

• Proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn).

• Familiarity with Pandas, NumPy, and basic data manipulation tools.

• Understanding of API development concepts.

• Exposure to containerization (e.g., Docker) and Linux environments.

• Experience with dashboarding or visualization tools (Power BI, Tableau, Dash, etc.).

• Familiarity with DevOps principles and tools.