ByteDance
Senior Site Reliability Engineer (Seed Model - Infrastructures - SRE)
Seattle, Washington · Senior · Full-time · Posted Oct 28, 2026
- Experience
- 3+ years in large-scale machine learning / generative AI MLOps or closely related infrastructure work.
- Education
- Bachelor's degree or above in Computer Science, Software Engineering or a related field.
About the job
Owns the reliability, performance, capacity, and cost efficiency of large-scale compute infrastructure supporting generative AI training and inference.
What you'll do
- Optimize training and inference systems, resource scheduling, heterogeneous clusters, high-speed networks, and storage.
- Plan and deliver compute capacity; build unified training and inference pools and improve utilization and cost control.
- Build monitoring, alerting, and automated remediation; resolve production issues and partner with teams on MLOps solutions.
What they ask for
- Bachelor's degree or above in Computer Science, Software Engineering, or a related field.
- Proficient in Python and/or Go; strong Linux skills and expert shell scripting.
- Expertise in Docker or Kata, Kubernetes, heterogeneous GPU/NPU clusters, resource scheduling, and large-scale AI training or inference stability and performance.
- Practical experience with RDMA networking and high-performance storage optimization; project experience with co-located training/inference pools or offline inference efficiency.
Nice to have
- End-to-end productionization of large-scale generative AI models; distributed training frameworks or inference optimization tooling.
- Multi-cloud or hybrid-cloud capacity planning, network/storage performance tuning, or distributed-system fault-injection experience.
Tools
Python, Go, Linux, Docker, Kata, Kubernetes, RDMA, GPU / NPU / XPU
AI focus
Reliability and performance engineering for large-scale generative AI training and inference infrastructure, including LLM, image, video, and multimodal workloads.
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