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 

Machine Learning & Advanced Analytics 

• 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. 

Data Engineering & Pipeline Support 

• 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. 

Cross‑Functional Collaboration 

• 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. 

Education and Experience 

• 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. 

Required Technical Skills 

• 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.