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.

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 Py Torch and modern ML tooling; familiarity with Hugging Face 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, Deep Speed, or Ray

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.