Senior Site Reliability Engineer; Seed Model - Infrastructures - SRE

Location:

Seattle

Team:

Technology

Employment Type:

Regular

Job Code:

A154484

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• Own the stability of training systems and heterogeneous inference systems for large-scale generative AI workloads (LLM, text-to-image, text-to-video, multimodal understanding); drive deep performance optimization and resolve core issues such as training interruptions, inference latency and throughput bottlenecks.
• Design and iterate a flexible, efficient resource scheduling system; improve scheduling efficiency, resource utilization and scheduling explainability; support fine-grained scheduling and priority management across workload types (training, offline inference, online serving).
• Lead the planning, deployment, operation and optimization of large-scale high-speed networks (RDMA) and heterogeneous compute clusters (GPU / NPU / XPU); ensure sound cluster topology, stable network bandwidth and efficient delivery of compute resources.
• Own the architecture, selection and operation of high-performance storage systems; ensure storage I/O performance, data reliability and stability to meet the data access needs of large-scale training and inference.
• Own end-to-end quality of capacity delivery; build capacity planning methodology, and combine business growth forecasts to complete resource expansion and configuration optimization so that capacity is delivered on time, reliably and cost-effectively.
• Drive the build-out of a unified compute pool with co-located training and inference workloads; improve compute utilization for offline inference and similar scenarios; establish an operating framework for compute efficiency and cost control, and codify reusable methodology.
• Safeguard the stability of complex online services and engineering architecture; build monitoring, alerting and automated remediation that cover the full training and inference path; respond to and resolve complex production issues quickly.
• Partner with algorithm, product and operations teams to deliver MLOps technical solutions; establish engineering best practices for large-scale generative AI, and raise the technical bar of the team.

• Bachelor's degree or above in Computer Science, Software Engineering or a related field, with 3+ years of experience in large-scale machine learning / generative AI MLOps or closely related infrastructure work.
• Proficient in Python and/or Go; solid Linux development and operations skills; expert in shell scripting and able to independently build automation tooling.
• Expert in containerization (Docker / Kata) and orchestration (Kubernetes), with hands-on experience deploying, operating and optimizing large-scale heterogeneous compute clusters (GPU / NPU).
• Familiar with the principles and operation of large-scale high-speed networking (RDMA) and high-performance storage (distributed / object storage), with practical experience optimizing these systems.
• Expert in the design and optimization of resource scheduling systems, with hands-on experience iterating scheduling policies and improving utilization; understands the key design considerations for scheduling explainability.
• Experience ensuring stability and optimizing performance of large-scale generative AI (LLM / image / video generation) training or inference systems; able to quickly localize and resolve complex issues across the full training and inference path.
• Project experience with co-located training / inference compute pools or offline inference efficiency; understands the core metrics and levers of compute operations.
• Strong communication, cross-team collaboration and self-drive; a strong sense of ownership; able to operate under the pressure of large-scale production environments.

• End-to-end experience product ionizing large-scale generative AI models (LLM, text-to-image, text-to-video).
• Experience managing heterogeneous compute clusters and capacity planning in multi-cloud or hybrid-cloud environments.
• Familiarity with distributed training frameworks and inference optimization tooling, with hands-on performance tuning experience.
• Experience with large-scale network performance tuning (RDMA bandwidth / latency) or breaking through storage I/O bottlenecks.
• Experience delivering large and complex compute capacity and building the associated operating framework, with quantified results in cost control or utilization improvement.
• Experience designing…