Principal AI Inference & Workload Optimization Lead

STN, Inc.; United States; A distancia; USD 180.000 - 280.000; Jornada completa

Descripción de la vacante

STN, Inc. seeks an Inference & Workload Optimization Lead to own how customer AI workloads run on their committed GPU capacity. You will profile workloads, establish baselines, and build agent-driven optimizations that scale across clusters.

The role involves collaborating with operating teams and advising on GPU allocation between internal and client workloads. Ideal candidates bring deep GPU performance engineering skills, experience with profiling tools (Nsight, DCGM) and distributed

Formación

• Deep GPU performance engineering: profiling, memory and bandwidth behaviour, kernel-level understanding.
• Explain performance findings to customers and encode for agents.
• Experience with distributed training/inference at scale and NCCL/tensor/pipeline/data-parallelism.

Responsabilidades

• Profile customer training and inference workloads to identify bottlenecks.
• Establish performance baselines per hardware generation (starting with current NVIDIA hardware).
• Turn expertise into agent-executable optimization to scale fixes across clusters.
• Work with committed-capacity customers on workload tuning.
• Own internal model serving as a secondary responsibility including cost and latency-aware routing and model migration.
• Advise on GPU allocation between internal inference and customer workloads.

Conocimientos

GPU performance engineering

Profiling and benchmarking

Distributed training/inference atScale

Explain performance findings to a non-

Herramientas

Nsight

DCGM

Descripción del empleo

STN, Inc. seeks an Inference & Workload Optimization Lead to own how customer AI workloads run on their committed GPU capacity. You will profile workloads, establish baselines, and build agent-driven optimizations that scale across clusters.

The role involves collaborating with operating teams and advising on GPU allocation between internal and client workloads. Ideal candidates bring deep GPU performance engineering skills, experience with profiling tools (Nsight, DCGM) and distributed