AI Native Systems Research Scientist Intern

IBM

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Work type: On-site
Location: —
Employment: Intern
Posted: 4 days ago
Freshness: Confirmed live today

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Similar $195k

$129k most similar roles pay here $263k

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IBM is a US-based global technology company providing hybrid cloud, AI, consulting, enterprise software, and IT infrastructure products and services.

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TL;DR · AI Native Systems Research Scientist Intern

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The AI Native Systems Research Scientist Intern 2027 joins a team focused on cutting-edge research in AI-native distributed systems. This role involves exploring and advancing various aspects of distributed computing, including the design, development, and optimization of software platforms for managing large language models and agents. The intern will work on high-performance distributed inference, networking solutions like RDMA over Ethernet, and infrastructure supporting the end-to-end AI lifecycle. Key responsibilities include formulating novel ideas, building prototype systems, and publishing results in scientific venues. Required skills include a strong foundation in machine learning, deep learning, and agentic patterns. Technical requirements include proficiency in Python, Go, C++, or Rust, along with experience in containerization using Docker or Kubernetes. The work addresses the technical challenges of scaling AI infrastructure and optimizing high-performance communication for distributed inference patterns.

Skills

Large Language Models Distributed Systems Python Go C++ Rust Docker Kubernetes RDMA Software-Defined Networking Linux Networking Deep Learning Machine Learning High-Performance Computing vLLM llm-d

What you'll do

Design, develop, and optimize software platforms for building and managing large language models (LLMs) and agents.
Develop frameworks and tools to support the end-to-end lifecycle of AI systems.
Implement high-performance distributed inference for LLMs and agentic workloads.
Prototype networking solutions for AI infrastructure, including RDMA over Ethernet and distributed inference patterns.
Evaluate network performance, scalability, and resiliency for emerging AI technologies.
Formulate novel research ideas and build prototype systems to solve complex computing challenges.
Publish research findings in leading scientific venues to demonstrate real-world impact.

What we're looking for

Master's Degree (preferred).
Currently pursuing a degree in Computer Science, Engineering, or a related field with a focus on AI, systems, networking, or software development.
Background in artificial intelligence and machine learning, including deep learning, LLMs, distributed LLM inference, or agentic patterns.
Proficiency in programming languages such as Python, Go, C++, or Rust.
Experience with containerization using Docker, Kubernetes, or other orchestration tools.
Understanding of LLM technology, distributed inference, llm-d/vLLM (preferred).
Experience in distributed systems, cloud/data center networking, SDN, network virtualization, Linux networking, or AI platform R&D (preferred).

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