ByteDance's Core Compute Infrastructure organization is building the next generation of cloud-native, GPU-optimized orchestration systems to deliver infrastructure that is highly performant, massively scalable, cost-efficient, and easy to use—enabling both internal and external developers to bring AI workloads from research to production at scale. The Inference Infrastructure team is expanding its focus on LLM inference infrastructure to support new AI workloads.
Requirements
- Solid knowledge of container and orchestration technologies (Docker, Kubernetes).
- Proficiency in at least one major programming language (Go, Rust, Python, or C++).
- 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.
Responsibilities
- Design and build large-scale, container-based cluster management and orchestration systems with extreme performance, scalability, and resilience.
- Architect 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.
Other
- Able to commit to working for 12 weeks during Summer 2026
- Excellent communication skills and ability to collaborate across global, cross-functional teams.
- Passion for system efficiency, performance optimization, and open-source innovation.
- Please state your availability clearly in your resume (Start date, End date).
- Summer Start Dates: - May 11th, 2026 - May 18th, 2026 - May 26th, 2026 - June 8th, 2026 - June 22nd, 2026
