AI/ML Solution Architect

AI/ML Solutions Architect – Distributed Training & GPU Infrastructure

Location: Remote from anywhere in the U.S. or Canada

Total compensation up to ~$500k (base + variable), depending on level and experience

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).

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

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 , making trade-offs at scale and translating customer needs into concrete platform requirements

Collaborate 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

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

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.