KLA-posted 13 days ago
0
Intern
Ann Arbor, MI
5,001-10,000 employees

Join KLA’s Service Supply Chain team as a Summer Intern in Machine Learning ! Our service supply chain supports a wide range of spare parts—from basic hardware to highly sophisticated optics, lasers, and precision mechanical devices. To ensure optimal delivery performance, we leverage advanced analytics and machine learning to predict demand and improve planning processes. As an intern, you will: Build a graph representation of KLA’s spare parts demand network to enable predictive modeling and other advanced analytics. Develop and implement machine learning models (e.g., using PyTorch) to forecast monthly spare parts demand. Explore and apply graph-based approaches for network representation and prediction. Work with large datasets to uncover patterns and insights that traditional analytics cannot easily reveal. Collaborate with the supply chain and planning teams to integrate findings into actionable strategies. Utilize Linux command line tools and scripting for data processing and workflow automation. This internship offers hands-on experience applying cutting-edge machine learning techniques to real-world supply chain challenges.

  • Build a graph representation of KLA’s spare parts demand network to enable predictive modeling and other advanced analytics.
  • Develop and implement machine learning models (e.g., using PyTorch) to forecast monthly spare parts demand.
  • Explore and apply graph-based approaches for network representation and prediction.
  • Work with large datasets to uncover patterns and insights that traditional analytics cannot easily reveal.
  • Collaborate with the supply chain and planning teams to integrate findings into actionable strategies.
  • Utilize Linux command line tools and scripting for data processing and workflow automation.
  • Enrollment in a graduate program (Master’s or Ph.D.) in a relevant field.
  • Demonstrated experience in developing and applying machine learning models.
  • Strong data wrangling and preprocessing skills (e.g., data cleaning and quality control).
  • Ability to learn new tools and technologies quickly.
  • Collaborative mindset and willingness to work in a team environment.
  • Strong proficiency in Python programming.
  • Experience with at least one machine learning framework (e.g., PyTorch, TensorFlow, Keras).
  • Familiarity with graph theory and its applications in machine learning is a plus.
  • Comfort with Linux command line and basic scripting.
  • Solid understanding of probability, statistics, and data analysis and visualization .
  • Ability to communicate technical concepts clearly to both technical and non-technical audiences.
  • medical
  • dental
  • vision
  • life, and other voluntary benefits
  • 401(K) including company matching
  • employee stock purchase program (ESPP)
  • student debt assistance
  • tuition reimbursement program
  • development and career growth opportunities and programs
  • financial planning benefits
  • wellness benefits including an employee assistance program (EAP)
  • paid time off and paid company holidays
  • family care and bonding leave
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