Our CTO Office AI Research Group is looking for a motivated advanced BSc student to join our team as a Data Preprocessing Engineer. You will play a foundational role in building the data infrastructure that powers our AI research - working directly with large-scale, real-world datasets at the intersection of machine learning and semiconductor chip design.
job requisition id
2604499
This is a hands-on, high-impact student position for someone who enjoys getting into the details of messy data and turning it into clean, structured, and model-ready assets.
What You Can Expect:
Collect, clean, normalize, and sanitize large-scale chip design datasets from diverse sources and formats
Build and maintain scripts and pipelines to automate repetitive data extraction and transformation tasks
Parse and process a wide variety of file formats (JSON, CSV, Parquet, logs, and domain-specific EDA formats)
Navigate complex codebases and directory structures to locate, understand, and extract relevant data
Work with structured and semi-structured data using SQL and Python-based tooling
Collaborate with researchers and engineers to understand data requirements and deliver model-ready datasets
Document data sources, transformation logic, and dataset versions in shared repositories.
Requirements: Advanced BSc student in Computer Science, Software Engineering, Electrical Engineering, Data Science, or a related field
Good Python skills with hands-on experience using pandas and NumPy for real data manipulation
Solid SQL and comfort working with structured and semi-structured data (JSON, CSV, Parquet, logs)
Practical experience cleaning, normalizing, and sanitizing messy, real-world data
Comfort navigating codebases and filesystems - understanding directory/file structures and parsing multiple file formats
Git fluency - reading commit history, diffs, and working with versioned repositories
Scripting ability to automate repetitive extraction and transformation tasks
Advantages:
Exposure to data pipeline tools or frameworks (e.g., Airflow, dbt, Spark)
Basic familiarity with ML concepts - understanding what makes a good feature, and awareness of train/test leakage
Curiosity for reverse-engineering undocumented data and uncovering hidden relationships across files and datasets.
This position is open to all candidates.