Algorithms/Machine Learning Engineering Intern (Multi-Modalities)

Responsibilities:

• Design and advance multi‑modality machine learning solutions that integrate diverse data sources to improve model performance.
• Lead theoretical investigations and data‑driven analyses to assess and validate new model architectures.
• Develop and maintain data‑quality evaluation frameworks for newly introduced modalities.
• Implement structured test plans and perform in‑depth analysis of experimental outcomes to guide iterative improvements.
• Partner with cross‑functional teams to refine algorithmic approaches and enhance measurement and evaluation capabilities.

Required Skills:

• Students studying towards a PhD Level Degree in Physics, Data Science, Machine Learning, Computer Science or related field that has worked with multi-modalities
• Proficiency in Python and Machine Learning frameworks (PyTorch or Tensorflow)
• Ability to perform experiments and data/error analysis
• Strong organizational skills and ability to follow detailed work plans and test procedures.

Preferred Qualifications:

• Publications on using Machine Learning for multi-modalities strongly encouraged
• Familiarity with materials science, engineering, and semiconductor processes are a plus

Minimum Qualifications:

• Students studying towards a PhD Level Degree in Physics, Data Science, Machine Learning, Computer Science or related field that has worked with multi-modalities
• Proficiency in Python and Machine Learning frameworks (PyTorch or Tensorflow)