Principal Solutions Architect
Nscale is a London-based, full-stack AI cloud and infrastructure company that provides GPU compute, managed AI services, orchestration software, data centers, and power infrastructure for AI training, fine-tuning, and inference.
Maintainer signals as of 9/25/2026
Funding history
About Nscale
Nscale builds and operates vertically integrated AI infrastructure spanning software, GPU compute, networking, storage, purpose-built data centers, and power. Its active cloud platform offers self-service inference endpoints, fine-tuning, managed Kubernetes and Slurm, virtual machines, and GPU clusters.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will design end-to-end GPU infrastructure for AI workloads, translate customer requirements into technical architectures, and advise on trade-offs. You will validate GPU cluster performance, troubleshoot infrastructure issues, automate design and deployment workflows, and define storage, monitoring, and operational-readiness requirements.
Requirements
- 10+ years of relevant solution architecture, infrastructure engineering, HPC, or large-scale data centre experience
- Experience designing large-scale GPU infrastructure and AI or HPC solutions
- Knowledge of GPU architecture, networking, bare-metal provisioning, Kubernetes, and Slurm
- Experience with NCCL and GPU performance validation tools
- Experience troubleshooting multi-node GPU clusters
- Python or Go scripting and automation skills
- Experience with Terraform and Ansible
- Knowledge of AI workload storage technologies
- Knowledge of PyTorch, DeepSpeed, or Megatron-style training stacks
Responsibilities
- Design end-to-end GPU infrastructure solutions
- Architect GPU clusters, network fabrics, storage, and orchestration layers
- Translate customer requirements into technical architectures and reference designs
- Advise customers on infrastructure design decisions and trade-offs
- Plan deployments and validate performance, reliability, and operational requirements
- Execute GPU-cluster acceptance and validation plans
- Benchmark GPU performance and troubleshoot multi-node cluster issues
- Automate architecture, bills of materials, network topologies, and deployment artefacts
- Use infrastructure-as-code and scripting to streamline deployment
- Define monitoring, observability, health-check, and operational-readiness requirements
- Research emerging GPU, networking, storage, and AI infrastructure technologies
Benefits
- Equity package
- Compensation reviews every 12 months
- Flexible work arrangements