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Intern – AI Model Efficiency (Quantization) System/Research Engineer

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Onsite

Intern – AI Model Efficiency (Quantization) System/Research Engineer

Qualcomm

Intern – AI Model Efficiency (Quantization) System/Research Engineer

Skills

Deep LearningPythonPyTorchAI

2 months ago

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Important information


Contract type:

Permanent

Location:

Seoul, South Korea

Work mode:

Onsite

Published on:

2 September 2026

What they need


Company Qualcomm Korea YH

Job Area Interns Group, Interns Group > Interim Engineering Intern - SW

Qualcomm Overview Qualcomm is a company of inventors that unlocked 5G ushering in an age of rapid acceleration in connectivity and new possibilities that will transform industries, create jobs, and enrich lives. But this is just the beginning. It takes inventive minds with diverse skills, backgrounds, and cultures to transform 5Gs potential into world-changing technologies and products. This is the Invention Age - and this is where you come in.

General Summary Our team in Seoul is developing AI technology that enables edge devices to run various models—including LLMs, VLMs, omni models, and more—efficiently. We work closely with teams in San Diego, Amsterdam, Beijing, Hanoi, and other global locations to deliver production-ready solutions that power next-generation devices.

We are looking for research interns with strong programming skills and a passion for quantization and model efficiency improvements.

What You'll Do (Responsibilities)

  • Implement, benchmark, and improve low-bit quantization methods (PTQ / QAT) for various AI models.
  • Contribute to evaluation and profiling pipelines, measuring accuracy and on-device performance (latency, throughput, memory) on Qualcomm hardware.
  • Prototype and optimize efficient inference kernels and model-compression techniques for edge deployment.
  • Collaborate with cross-functional and global teams to move research prototypes toward production-ready solutions.
  • Document findings clearly and present results to the team.

Minimum Qualifications

  • Currently pursuing a BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field.
  • Proficiency in Python.
  • Hands-on experience with deep learning frameworks (e.g., PyTorch).
  • Strong problem-solving and debugging skills.

Preferred Qualifications

  • Understanding of quantization techniques and model compression.
  • Good software design fundamentals.
  • Familiarity with Triton kernels and CUDA programming.
  • Experience profiling or optimizing models for efficient inference.

Applicants : Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail [email protected] or call Qualcomm's toll-free number found here . Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities.

Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.

To all Staffing and Recruiting Agencies: Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.

If you would like more information about this role, please contact Qualcomm Careers.

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