Senior AI Compute Infrastructure Engineer

📋 Description Own and operate GPU and accelerator clusters for training, inference, and experimentation. Design infra to run models locally on GPUs, reducing external dependencies and costs. Build scheduling, orchestration, placement, quota, and utilization across heterogeneous accelerators. Optimize inference pipelines for latency, throughput, memory, and cost (vLLM/Triton/TensorRT). Partner with ML researchers to remove bottlenecks in training, evaluation, and online inference. Build observability for GPU usage, memory pressure, queues, latency, and spend. 🎯 Requirements 5+ years of infrastructure engineering experience with GPU compute, ML infra, distributed systems, HPC, or large-scale production platforms. Hands-on experience operating GPU clusters or accelerator-backed infra, including scheduling, orchestration, utilization monitoring, and cost optimization. Strong systems engineering fundamentals across Linux, networking, storage, containers, Kubernetes, distributed runtimes, and production debugging. Experience with ML serving frameworks such as vLLM, Triton Inference Server, TensorRT, TorchServe, KServe, Ray Serve, or equivalents. Proficiency in Python for infrastructure automation, tooling, debugging, integration, and operational workflows. Practical understanding of performance tradeoffs across batching, concurrency, memory usage, GPU utilization, latency, throughput, availability, and cost. 🎁 Benefits Global, merit-based culture with equal opportunity. Remote-friendly work with an international team. Opportunity to build cutting-edge AI compute infra for Kraken. Diverse, world-spanning Payward family.