Thermal Engineering R&D Intern

Description:

We are seeking a motivated engineering intern to support the development of an AI/ML-driven system for thermal monitoring, prediction, and anomaly detection during semi-conductor tests. The intern will focus primarily on machine learning model development and evaluation, data analysis, model experimentation, and software implementation, working with thermal system telemetry data to identify optimal modeling approaches.

Responsibilities

• Model Development & Prototyping: Research, develop, and test ML models (Regression, Time-Series, Anomaly Detection) using Python frameworks like TensorFlow, PyTorch, or Scikit-learn to improve thermal monitoring and prediction.
• Data Engineering & Pipeline Support: Build and automate data pipelines, including preprocessing, feature engineering, and labeling of thermal telemetry data for model training.
• Validation & Benchmarking: Conduct simulation-based validation and performance benchmarking to compare algorithms, ensuring model accuracy against real-world measured system behavior.
• Telemetry Analysis & Visualization: Analyze and visualize large datasets to identify patterns and performance anomalies, helping refine predictive capabilities.
• Hardware & Systems Integration: Collaborate with cross-functional engineering teams to acquire system logs, configure test setups, and integrate ML models into the broader software architecture.
• Documentation & Reporting: Maintain clear documentation of model assumptions, training methodologies, and results to support future model selection and deployment.