GenAI/ML Systems Research Intern

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.

Your responsibilities

• 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.

Your skills and experience

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