AI/ML Intern

Description du poste:

Number of Position(s): 2
Duration: 10 Weeks
Date: June - August, 2026
Location: On-site, in Murray Hill, New Jersey.

Education Recommendations

Eligible candidates should currently be pursuing a Master's or Ph.D. in Computer Science, Computer Engineering, or a related field with an accredited school in the US.

The selected candidate will have the opportunity to contribute to a Machine Learning Operations (MLOps) platform, which supports state-of-the-art training and inference features with a focus on sustainable MLOps practices.
The selected candidate will have the opportunity to contribute to a GenAI and AI/ML systems. The selected candidates will work on:

* Advanced AI/ML systems with a focus on next-generation model training, high-performance inference, and intelligent workload orchestration across heterogeneous compute environments.
* Building and optimizing LLM-based systems, designing distributed inference workflows across cloud, edge, RAN, or vehicular platforms.
* Exploring how workload characteristics, model behavior, and system conditions influence latency, throughput, and efficiency.
* Contribute to experimental prototypes, performance analysis, and cross-cluster or multi-tier execution frameworks that support emerging AI applications.
* Reliability, observability, and interpretability aspects of AI-enabled network operations, including system modeling and inference-time interventions for multimodal transformers.
* Collaborate with experienced researchers to investigate real-world constraints such as resource heterogeneity, network dynamics, mobility, and performance variability-helping shape platforms that deliver robust, responsive, and efficient AI services

Profil requis du candidat:

We are looking for students with the following background and skillset.
* Advanced AI/ML systems with a focus on next-generation model training.
* Building and optimizing LLM-based systems.
* Strong programming ability in Python; experience with C++, Go, or Java is a plus.
* Solid fundamentals in computer systems, networking, and Linux/Unix environments.
* Experience with PyTorch and modern ML tooling; familiarity with HuggingFace ecosystem.
* Understanding of deep learning, specifically Transformer architectures.
* Exposure to distributed systems, containers, and orchestration tools (Docker, Kubernetes).
* Ability to design experiments, analyze performance, and debug complex system interactions.
* Experience with vLLM, SGLang, TGI, TensorRT-LLM, llama.cpp, DeepSpeed, or Ray