Software Engineer Graduate (Inference Infrastructure) - 2026 Start (PhD)

About the role

The Inference Infrastructure team is the creator and open-source maintainer of AIBrix, a Kubernetes-native control plane for large-scale LLM inference. We are part of ByteDance’s Core Compute Infrastructure organization, responsible for designing and operating the platforms that power microservices, big data, distributed storage, machine learning training and inference, and edge computing across multi-cloud and global datacenters.

We are expanding our focus on LLM inference infrastructure to support new AI workloads, and are looking for engineers passionate about cloud-native systems, scheduling, and GPU acceleration. You’ll work in a hyper-scale environment, collaborate with world-class engineers, contribute to the open-source community, and help shape the future of AI inference infrastructure globally.

Responsibilities

• Design and build large-scale, container-based cluster management and orchestration systems with extreme performance, scalability, and resilience.
• Arcitect next-generation cloud-native GPU and AI accelerator infrastructure to deliver cost-efficient and secure ML platforms.
• Collaborate across teams to deliver world-class inference solutions using vLLM, SGLang, TensorRT-LLM, and other LLM engines.
• Stay current with the latest advances in open source (Kubernetes, Ray, etc.), AI/ML and LLM infrastructure, and systems research; integrate best practices into production systems.
• Write high-quality, production-ready code that is maintainable, testable, and scalable.

Qualifications

• Minimum Qualifications: Individuals who are completing or have recently completed a PhD degree in Software Development, Computer Science, Computer Engineering, or a related technical discipline. Strong understanding of large model inference, distributed and parallel systems, and/or high-performance networking systems. Hands-on experience building cloud or ML infrastructure in areas such as resource management, scheduling, request routing, monitoring, or orchestration. Solid knowledge of container and orchestration technologies (Docker, Kubernetes).
• Preferred Qualifications: Experience contributing to or operating large-scale cluster management systems (e.g., Kubernetes, Ray). Experience with workload scheduling, GPU orchestration, scaling, and isolation in production environments. Hands-on experience with GPU programming (CUDA) or inference engines (vLLM, SGLang, TensorRT-LLM). Familiarity with public cloud providers (AWS, Azure, GCP) and their ML platforms (SageMaker, Azure ML, Vertex AI). Strong knowledge of ML systems (Ray, DeepSpeed, PyTorch) and distributed training/inference platforms. Excellent communication skills and ability to collaborate across global, cross-functional teams. Passion for system efficiency, performance optimization, and open-source innovation.