/ ABOUT THE ROLE /

Join a fast-moving AI infrastructure team working on the cutting edge of large-scale ML workloads. This role is ideal for engineers who enjoy solving deep technical challenges in distributed training, multi-GPU systems, and scalable AI inference infrastructure. You will work directly with AI-focused clients, helping them get the most out of modern GPUs (H100, B200, etc.) and ML frameworks such as PyTorch (and JAX in some environments).

/ TEAM & RESPONSIBILITIES /

Work alongside senior AI and infrastructure engineers building large-scale GPU platforms. As part of the customer solutions team, you will design and validate production-grade distributed training (primary) and large-scale inference architectures on large GPU clusters, typically tens to thousands of GPUs, and work hands-on with customers to debug, optimize, and scale ML workloads across multi-node GPU environments. You will act as a technical authority on GPU performance, networking, and schedulers, making trade-offs at scale and translating customer needs into concrete platform requirements, while collaborating closely with engineering, product, and R&D to influence roadmap decisions based on real-world ML workloads. This is a hands-on, technical role; you are expected to work directly in customer environments, not only advise at a high level.

/ REQUIRED SKILLS /

Candidates need hands-on experience designing and operating production-grade, multi-node GPU workloads for training or inference, along with a strong background in distributed deep learning (PyTorch Distributed, DeepSpeed) on GPU clusters. A deep understanding of GPU architecture and interconnects (H100/A100 class, NVLink, InfiniBand) is required, as is experience with Kubernetes or Slurm and performance tuning using GPU profiling and monitoring tools.

This role is not a fit if your experience is limited to single-node training, high-level AI strategy, or non-production research environments. We are looking for engineers and architects who thrive at the intersection of AI workloads and large-scale infrastructure.