Join a fast-moving AI infrastructure team working on large-scale ML workloads
Design and validate production-grade distributed training and large-scale inference architectures on large GPU clusters
Work hands-on with customers to debug, optimize, and scale ML workloads across multi-node GPU environments
Act as a technical authority on GPU performance, networking, and schedulers
Collaborate closely with engineering, product, and R&D to influence roadmap decisions

Hands-on experience designing and operating production-grade, multi-node GPU workloads for training or inference
Strong background in distributed deep learning (PyTorch Distributed, DeepSpeed) on GPU clusters
Deep understanding of GPU architecture and interconnects (H100/A100 class, NVLink, InfiniBand)
Experience with Kubernetes or Slurm and performance tuning using GPU profiling and monitoring tools
Must be fluent in English.

Solutions Architect – AI/ML, Training & GPU Infra

Key skills