Cost-Efficient Inference Serving and Routing Optimization- MSc and PHD-Summer internship 2026- Research Lab

Introduction

At IBM work is more than a job - it’s a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you’ve never thought possible. Are you ready to lead in this new era of technology and solve some of the world’s most challenging problems? If so, let’s talk.

Your Role And Responsibilities

We are looking for a highly motivated PhD or MSc student to join our team for a summer internship focused on cost-efficient serving of large-scale AI inference workloads.

The internship will explore advanced routing strategies and KV-cache–aware optimizations in distributed inference systems, with an emphasis on improving performance, scalability, and GPU cost efficiency.

What you will work on

• Designing and evaluating routing algorithms to optimize inference latency, throughput, and cost
• Investigating KV cache management strategies for large-scale, distributed inference serving
• Prototyping, benchmarking, and analyzing inference optimization techniques
• Working with modern inference frameworks and real production-like workloads
  
  

• MSc or PhD student in Computer Science, Machine Learning Systems, or a related field
• Strong background or interest in distributed systems, systems research, or ML infrastructure
• Strong programming skills (Python, Go, or similar)
• Hands-on experience or familiarity with vLLM (architecture, KV cache behavior, scheduling, or extensions)
• Interest in AI infrastructure, performance optimization, and cost efficiency
• Ability to work independently while collaborating effectively within a research and engineering team
  
  

• Experience with Kubernetes (K8s) and cloud-native systems
• Familiarity with inference serving stacks, networking, or GPU-based systems
• Experience with benchmarking, profiling, or performance analysis