GenAI​/ML Systems Research Intern

Job Overview

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 U.S.

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.

• 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.

• 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, Tensor
  
  RT‑LLM, llama.cpp, Deep Speed, or Ray.

We act inclusively and respect the uniqueness of people. Our employment decisions are made regardless of race, color, national or ethnic origin, religion, gender, sexual orientation, gender identity or expression, age, marital status, disability, protected veteran status, or other characteristics protected by law. We are committed to a culture of inclusion built upon our core value of respect.

If you’re interested in this role but don’t meet every listed requirement, we still encourage you to apply. Unique backgrounds, perspectives, and experiences enrich our teams, and you may be the right candidate for this or another opportunity.