AI Native Systems Research Scientist Intern 2027

At IBM Research, we are the innovation engine of IBM. Exploring what’s next in computing and shaping the technologies the world will rely on tomorrow. From advancing AI and hybrid cloud to pioneering practical quantum computing, we anticipate challenges and unlock new opportunities for clients, partners, and society. Working in Research means joining a team that accelerates discovery at the intersection of high-performance computing, AI, quantum, and cloud. You’ll collaborate with leading scientists, engineers, and visionaries to push boundaries and turn ideas into reality. With a culture built on curiosity, creativity, and collaboration, IBM Research offers the opportunity to grow your career while contributing to breakthroughs that transform industries and change the world. Join a team conducting cutting-edge research in AI Native Distributed Systems. In this role, you will explore and advance one or more of many aspects of AI-native distributed computing we are currently working on, including: design, development, and optimization of software platforms for building, deploying, and managing large language models (LLMs) and agents; principles, methodologies, and frameworks for AI-native software development, evolution, and optimization; high-performance distributed inference for LLMs; frameworks, tools, and infrastructure that support the end-to-end AI lifecycle; networking for AI infrastructure, developing/prototyping networking solutions and techniques for future AI systems, including networking for distributed inference and RDMA over Ethernet networking, as well as evaluation of the solutions from performance, scalability, and resiliency perspectives; networking innovations that enable high-performance communication for AI workloads and emerging distributed inference patterns. The ideal candidate has a strong foundation in AI, large language models, and agentic workloads, combined with expertise in systems, networking, or software engineering. As an intern, you will contribute across the full industrial research lifecycle: formulating novel ideas, designing and building prototype systems, evaluating performance at scale, demonstrating real-world impact, and publishing results in leading scientific venues. Currently pursuing a degree in Computer Science, Engineering, or related field (MS/PhD), with a focus on artificial intelligence, systems, networking, or software development. Background in artificial intelligence and machine learning, including areas such as deep learning, LLMs, distributed LLM inference, or agentic patterns Strong background in software development, including proficiency in languages such as Python, Go, C++, or Rust Experience with containerization using Docker, Kubernetes, or other container orchestration tools. Understanding of LLM technology, distributed inference, llm-d/vLLM Designing and implementing distributed systems Experience in cloud/data Center networking, Software-Defined Networking (SDN), network virtualization, or Linux networking Research and development experience in AI platforms, including the design, implementation, and optimization of AI frameworks and tools