Intern, Distributed Inference for Multi-Modal Large Language Models (

About InterDigital

InterDigital is a global research and development company focused primarily on wireless, video, artificial intelligence (“AI”), and related technologies. We design and develop foundational technologies that enable connected, immersive experiences in a broad range of communications and entertainment products and services. We license our innovations worldwide to companies providing such products and services, including makers of wireless communications devices, consumer electronics, IoT devices, cars and other motor vehicles, and providers of cloud-based services such as video streaming. As a leader in wireless technology, our engineers have designed and developed a wide range of innovations that are used in wireless products and networks, from the earliest digital cellular systems to 5G and today’s most advanced Wi-Fi technologies. We are also a leader in video processing and video encoding/decoding technology, with a significant AI research effort that intersects with both wireless and video technologies. Founded in 1972, InterDigital is listed on Nasdaq.

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Summary

Multi-Modal Large Language Models (MLLMs) are increasingly capable of processing and reasoning over diverse input modalities such as text, images, audio, and video. However, running such models in real-time or resource-constrained environments poses significant challenges in terms of bandwidth and compute requirements.

Distributing the processing of models between client and server (a "distributed computing" approach) is a promising solution. While traditional distributed inferencing has been applied successfully to DNN models, extending this paradigm to MLLMs is a novel and impactful use case.

This internship aims to demonstrate the feasibility of distributed MLLMs inference approach, where MLLM components are distributed across two endpoints, coordinated through an agentic orchestration.

Responsibilities

The internship will be involved in the following tasks:

Survey the recent advances in MLLMs,

Select representative models, and a representative agentic architecture in collaboration with the Team

Implement the selected models as a reference platform,

Propose one or more distributed inference strategies,

Implement and adapt these strategies in Python,

Conduct experiments to measure key performance metrics such as latency, data size, and energy consumption

Qualifications

Education: Master’s student in Computer Science, Artificial Intelligence, Data Science, or related field.

Skills:

Background in AI/ML, particularly large language models.

Knowledge of multi-modal systems (text, vision, speech).

Proficiency in Python programming and ML frameworks (PyTorch).

Ability to conduct research and prototype efficiently.

Nice to have: familiarity with distributed systems, networking, bandwidth concepts, ONNX framework

Keywords:

MLLM (Multi-Modal Large Language Model), Distributed inference, Python prototyping

Expected Outcomes:

Hands-on experience with state-of-the-art MLLMs

Development of agentic based proof of concept

Potential participation in one scientific paper, publications and patents

Location: Rennes, France

InterDigital is an equal employment opportunity employer. InterDigital will not engage in or tolerate unlawful discrimination with regard to any employment decision, policy or practice based on a person’s sex, gender, pregnancy (including childbirth, breastfeeding and related medical conditions), age, race, color, religion, creed, national origin, ancestry, citizenship, military status, veteran status, mental or physical disability, medical condition, genetic information, sexual orientation, gender identity or expression, or any other factor protected by applicable federal, state or local law. This policy applies to all terms and conditions of employment, including, but not limited to, recruiting, hiring, compensation, benefits, training, assignments, evaluations, coaching, promotion, discipline, discharge and layoff.

REQ25-1039