AI Research Engineering Intern

19 Minutes ago • All levels • $63,000 PA - $166,000 PA

Job Summary

Job Description

This internship focuses on Transformer-based Deep Learning Classification Methods for semiconductor defect dispositioning. The intern will select and evaluate Transformer Networks, and compare them with traditional approaches. The role involves examining Conditional Classification Models to reduce model retraining time and cost, and exploring explainability tools such as enhanced GradCam and deconvolutional methods. The intern will use datasets from established projects. The intern will work on tasks related to AI research and deep learning applications in the semiconductor industry. This role is for an intern who is expected to contribute to research and development in the field of AI, with a focus on practical applications and problem-solving.
Must have:
  • Currently enrolled in a relevant accredited university program.
  • Successfully completed up to and including year 2 of a PhD program.
  • Familiarity with deep learning program environments (TensorFlow, PyTorch).
  • Proficiency in Python and its visualization packages.
Good to have:
  • Prior experience in evaluating deep learning methods for industrial problems.
Perks:
  • Competitive pay
  • Stock options
  • Bonuses
  • Health benefits
  • Retirement plans
  • Vacation time

Job Details

Job Details:

Job Description: 

This internship will examine Transformer-based Deep Learning Classification Methods for semiconductor defect dispositioning. In the literature in recent years, Visual Image Transformer-based classification networks have been proposed to provide additional model capabilities beyond what traditional Convolutional Neural Networks can provide. In addition to the possibility of increased accuracy, Transformer-based networks can execute object detection tasks and potentially do so with self-supervised training.

A deliverable from the internship will be to select potential Transformer Networks from the literature and evaluate them alongside the traditional approaches our team employs. The intern can use data sets from established projects. Conditional Classification Models are another area of potential benefit for our defect classification work.

Traditionally, models require retraining in order to contextualize the model for a given product. By leveraging a conditional classification approach, it may be possible to retrain only those weights of the model relevant to the product topology changes, yet reuse the other common weights for other detection aspects. This would reduce the training time of the model and reduce overall cost of ownership.

Lastly, the internship will include examining explainability tools beyond the traditional GradCam methods and look at enhanced GradCam and deconvolutional methods to better understand network attention.

Qualifications:

  • Candidate must be currently enrolled in an accredited college or university in industrial engineering, electrical engineering, or computer science/engineering, or similar degree program.

  • Must have successfully completed up to and including year 2 of a PhD program.

  • Must be familiar with deep learning program environments such as TensorFlow, PyTorch, ONNX, and OpenVINO.

  • Must be proficient in Python, and visualization packages therein.

  • Candidates with prior experience in evaluating deep learning methods for industrially relevant problems are preferred.

          

Job Type:

Student / Intern

Shift:

Shift 1 (United States of America)

Primary Location: 

US, Arizona, Phoenix

Additional Locations:

Business group:

Intel Foundry is dedicated to transforming the global semiconductor industry by delivering cutting-edge silicon process and packaging technology leadership for the AI era. As stewards of Moore's Law, we innovate and foster collaboration within an extensive partner ecosystem to advance technologies and enable our customers to design leadership products. Our strategic investments in geographically diverse manufacturing capacities bolster the resilience of the semiconductor supply chain. Leveraging our technological prowess, expansive manufacturing scale, and a more sustainable supply chain, Intel Foundry empowers the world to deliver essential computing, server, mobile, networking, and automotive systems for the AI era. This position is part of the Foundry Services business unit within Intel Foundry, a customer-oriented service organization that is dedicated to the success of its customers with full P&L responsibilities. We ensure our foundry customers' products receive our utmost focus in terms of service, technology enablement and capacity commitments.

Posting Statement:

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

Position of Trust

N/A

Benefits:

We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock, bonuses, as well as, benefit programs which include health, retirement, and vacation.  Find more information about all of our Amazing Benefits here:

https://intel.wd1.myworkdayjobs.com/External/page/1025c144664a100150b4b1665c750003

Annual Salary Range for jobs which could be performed in the US:

$63,000.00-$166,000.00

Salary range dependent on a number of factors including location and experience.

Work Model for this Role

This role will require an on-site presence. * Job posting details (such as work model, location or time type) are subject to change.

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About The Company

Intel provides reasonable accommodation to applicants and employees. For more information on our Reasonable Accommodation process, please clickhere.When you use this site, Intel Corporation uses cookies to improve your online experience. For more information, visit ourCookie Notice.To view our candidate privacy notice, please clickhere.

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