Undergraduate Intern Technical (OpenVINO, Neural Network Compression Tools)

Intel Corporation

Leixlip

Hybrid

EUR 28,000 - 35,000

Part time

9 days ago

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Benefits offered by this job

Hybrid work model

Job summary

Intel Corporation in Leixlip, Ireland, invites undergraduates to join the Neural Network Compression Tools team within the OpenVINO organization for a six-month internship. You will work on developing the Neural Network Compression Framework (NNCF) and explore compression algorithms to optimize neural network inference on Intel platforms.

The role emphasizes learning SOTA models and architectures, with Python as a primary language.

Qualifications

  • Undergraduate student working towards BSc or MSc; 3rd year or higher.
  • Preferred majors: Math, Computer Science or Physics.
  • Proficient in Python programming.
  • Optional: knowledge of ML/NN, image processing or computer vision.
  • Familiar with PyTorch, TensorFlow, OpenVINO.

Responsibilities

  • Contribute to development of Neural Network Compression Framework (NNCF).
  • Explore and implement state-of-the-art NN compression algorithms for inference optimization in OpenVINO.
  • Learn SOTA models/architectures and Intel platform specifics.

Skills

Python programming
Machine Learning
Neural networks
Computer Vision

Education

BSc or MSc (3rd year or higher)

Tools

PyTorch
TensorFlow
OpenVINO

Job description

## Undergraduate Intern Technical (OpenVINO, Neural Network Compression Tools)Apply: Ireland, Leixlip: Part time: Posted Today: JR0287250# **Job Details:**## Job Description:We are looking for talented students interested in Computer Science and Software Engineering in the Deep Learning area. You will join the Neural Network Compression Tools team in OpenVINO organization. In this position, you will be involved in the development of the Neural Network Compression Framework (NNCF). The responsibilities will include exploration and development of state-of-the-art (SOTA) compression algorithms for neural network inference optimization in OpenVINO. You will have an opportunity to learn SOTA models and architectures, compression algorithms, the most popular frameworks and get knowledge of Intel platforms specifics. ***You must be available for at least six months to qualify for the internship.***## **Qualifications:*** Undergraduate university student working towards BSc or MSc: 3rd year or higher.* Preferred major: Math, Computer Science or Physics.* Python programming skills.Optional:* Knowledge of Machine Learning / Neural Networks, Image Processing / Computer Vision.* PyTorch, TensorFlow, OpenVINO.## Job Type:Student / Intern## Shift:Shift 1 (Ireland)## Primary Location:Ireland, Leixlip## Additional Locations:## 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 TrustN/A## EU SalaryWe offer a total compensation package that ranks among the best in the industry.The minimum Annual Salary for this job which could be performed in Ireland is: €31,400.00The job posting reflects the minimum base compensation for this role. Our standard internship rates are aligned with the specific job role and degree level. As part of our total compensation approach, the final offer may also include additional pay components and benefits. Your recruiting team will provide the confirmed base salary along with any applicable total compensation elements upon employment offer delivery.**Work Model for this Role**This role will be eligible for our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. \\* Job posting details (such as work model, location or time type) are subject to change.
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