Generative AI Applications Intern at Intel

Job Description:

This position requires candidates to upload a resume in English; you are welcome to upload multiple versions of your resume if you prefer but an English version of your resume will be required to be considered for this position.

IATG group in Mexico is seeking a motivated student to explore and implement cutting-edge applications of Large Language Models on Intel hardware, with a special emphasis on FPGA-based solutions and engineering knowledge graphs. As part of this internship, you will be part of a small Research and Development team, that path finds next-gen technologies to improve Intel's products. Your responsibilities will include but not limited to:Work on pathfinding projects on how to improve the efficiency of RTL design for FPGA and ASIC flows. Develop FPGA-based AI prototypes leveraging Intel FPGAs (e.g., Stratix, Agilex) by investigating accelerated inference workflows. Work on bringing up modern LLMs on Intel hardware for local infrastructure and benchmark different model architectures for latency, throughput, and energy efficiency.

Qualifications:

Minimum qualifications are required to be initially considered for this position. Preferred qualifications are in addition to the minimum requirements and are considered a plus factor in identifying top candidates.

Minimum Qualifications:

• Pursuing a PhD degree program in Electrical Engineering, Computer Science, or related fields.
• 1+ year of experience in:
  • FPGA design flows (Intel Quartus or similar) and hardware description languages (VHD, Verilog or SystemVerilog).
  • Machine learning frameworks (Pytorch, TensorFlow) and Python scripting language.
• Advance English level.
• Must have unrestricted, permanent right to work in Mexico (this role is not eligible for visa or immigration sponsorship).

Preferred Qualifications:

• Familiar with oneAPI, OpenVINO or similar Intel toolchains.
• Experience deploying LLMs on clusters.
• Exposure to LLM architectures and large-scale deep learning models.

Requirements listed would be obtained through a combination of industry relevant job experience, internship experiences and or schoolwork/classes/research.