AI/ML Process Engineering Intern (Spring 2027)
About The Position
First Solar is seeking an AI/ML Process Engineering Intern for Spring 2027. This internship offers a unique opportunity to contribute to the world's sustainable energy future by working on real-world projects in research and development. Interns at First Solar are challenged daily, gain practical experience, and are encouraged to find innovative solutions and develop new skills. The company fosters a culture of innovation through collaboration, smart risk-taking, learning from failures, and personal accountability. The internship is expected to last 6 months, spanning Spring/Summer or Summer/Fall semesters, with flexibility for interns to enroll in classes as long as it doesn't interfere with job functions.
Requirements
- Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Electrical/Computer Engineering, or a related field.
- Upper-level undergraduate (junior/senior) or graduate student.
- Coursework or project experience applying machine learning to image data (e.g., CNNs, transfer learning).
- Working proficiency in Python.
- Proficiency in Python and standard ML frameworks (PyTorch, TensorFlow, or scikit-learn).
- Understanding of image classification and transfer learning.
- Basic statistics and experimental design.
- Ability to build and document reproducible data pipelines.
- Strong communication skills to work effectively with an engineer mentor.
Nice To Haves
- Experience with Bayesian optimization or Gaussian Process modeling (e.g., Ax/BoTorch).
- Experience with OpenCV or similar image-processing libraries.
- Materials science/thin-film/photovoltaics background.
Responsibilities
- Build machine-learning tools that support perovskite process development and quality control.
- Train a transfer-learning computer vision model to identify defects in perovskite device images.
- Build a Gaussian Process surrogate model to propose next experiments for precursor ratio, annealing profile, and coating parameters.
- Deliver a reusable closed-loop optimization framework.
- Extend the defect library and layer a statistical process control (SPC) framework on the classifier's output (if time allows).
- Present accomplishments and project summary to the leadership team upon completion.
Benefits
- Real-world experience
- Opportunity to find new solutions and develop new skills
- Flexibility to enroll in classes
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What This Job Offers
Career Level
Intern
