Machine Learning Engineering Student

The Wireless Connectivity AI Center of Excellence is seeking a Machine Learning Engineering Student. This role involves developing large-scale generative AI framework software, preparing data for ML models, building inference interfaces, enabling ML Ops for continuous delivery, and creating sustainable AI productization platforms. The student will also lead the creation of machine learning workflows and infrastructure necessary to build AI models and demonstrate solution feasibility.

Must Have

• BSc/MSc/PhD student in Computer Science or Electrical Engineering or similar
• Ability to work at least 20 hours a week, with studies expected to continue for at least 1.5 years
• Practical programming experience in Python
• Solid understanding of machine learning techniques and applications
• Proficiency in developing large-scale models using AI frameworks (e.g., PyTorch, TensorFlow)
• Development of large-scale generative AI framework software
• Preparing data for machine learning models at scale
• Building inference interfaces for ML model consumption
• Enabling ML Ops for continuous delivery and automation of ML pipelines
• Developing solutions that can evolve into sustainable AI productization platforms
• Leading the creation of machine learning workflows and infrastructure

Good to Have

• Practical programming experience in C#
• Experience with Azure Generative AI (ChatGPT) Cognitive AI services

Job Description

The Wireless Connectivity AI Center of Excellence is seeking a Machine Learning Engineering Student to join our dynamic team.

In this role, you will be responsible for:

• Development of large-scale generative AI framework software development
• Preparing data for machine learning models at scale.
• Building inference interfaces for ML model consumption.
• Enabling ML Ops for continuous delivery and automation of ML pipelines.
• Developing solutions that can evolve into sustainable AI productization platforms.
• Leading the creation of machine learning workflows and infrastructure necessary to build AI models and demonstrate solution feasibility.

Qualifications

• BSc/MSc/PhD student in Computer Science or Electrical Engineering or similar.
• Ability to work at least 20 hours a week (one day from Petach Tikva offices), with studies expected to continue for at least 1.5 years.
• Practical programming experience in Python and ideally also C# is required.
• Experience with Azure Generative AI (ChatGPT) Cognitive AI services is highly desired.
• Solid understanding of machine learning techniques and applications.
• Proficiency in developing large-scale models using widely utilized AI frameworks (e.g., PyTorch, TensorFlow).

The ideal candidate enjoys solving problems, possesses excellent communication and technical skills, is motivated, proactive, a self-starter, and has a strong curiosity for exploring emerging AI technologies and realizing novel use cases, with hands-on experience in software development.

#LI-DNP

Job Type:

Student / Intern

Shift:

Shift 1 (Israel)

Primary Location:

Israel, Petah-Tikva

Additional Locations:

Business group:

The Silicon Engineering Group (SIG) is a worldwide organization focused on the development and integration of SOCs, Cores, and critical IPs from architecture to manufacturing readiness that power Intel’s leadership products. This business group leverages an incomparable mix of experts with different backgrounds, cultures, perspectives, and experiences to unleash the most innovative, amazing, and exciting computing experiences.

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

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.