About Radiant
Radiant is redefining how AI infrastructure is built. We design and operate AI-native infrastructure platforms engineered for sovereignty, performance, and scale — powering GPU-native workloads, multi-tenant control planes, and high-performance AI systems for the most demanding environments. We are building purpose-built AI infrastructure from powered land, to compute, to software. As we scale our operations and deploy capital into the next generation of AI infrastructure, we are looking to expand our product team with leaders who can combine technical strength with execution excellence and are driven to build.
Radiant was established by Brookfield, a leading global alternative asset manager with over US$1 trillion of assets under management across real estate, infrastructure, renewable power and transition, private equity and credit. Brookfield's global relationships, investment expertise and access to long-term institutional capital provide Radiant with a differentiated platform from which to develop, finance and operate AI infrastructure assets. This combination of entrepreneurial execution and institutional sponsorship enables Radiant to pursue large-scale GPU and AI infrastructure opportunities globally.
As a Senior/Principal Product Manager - Cluster Platform, you will own the product experience and operational lifecycle for bringing new AI infrastructure clusters onto Radiant's platform. You'll work across cluster onboarding, acceptance, validation, benchmarking, provisioning and automation, partnering closely with infrastructure, SRE, networking, storage, platform engineering, operations, and hardware teams. This is a highly technical product role focused on making AI cluster deployment repeatable, measurable, automated, and production-ready at scale.
Responsibilities
•Own the strategy, roadmap, and delivery for cluster onboarding, validation, acceptance, and lifecycle management.
•Define the end-to-end process for taking new infrastructure from installed hardware to production-ready cluster.
•Productise and automate workflows across discovery, provisioning, configuration, testing, benchmarking, acceptance, and handover.
•Define cluster readiness and acceptance criteria across compute, GPUs, networking, storage, firmware, software, and reliability.
•Partner with engineering to build tooling and automation for cluster deployment, validation, remediation, and lifecycle operations.
•Define benchmarking frameworks for GPU performance, network fabric, storage throughput, collective communication, and workload performance.
•Build clear visibility into cluster state, readiness, failures, exceptions, and deployment progress.
•Identify recurring deployment friction and turn operational processes into scalable platform capabilities.
•Drive prioritisation, technical requirements, delivery, adoption, and continuous improvement across the cluster lifecycle.
•Define success metrics across time-to-ready, automation coverage, acceptance pass rates, deployment reliability, and cluster performance consistency.
Requirements
•Experience owning technical infrastructure, cloud platform, data centre, HPC, or AI infrastructure products.
•Strong understanding of the lifecycle involved in deploying and operating large-scale compute or GPU clusters.
•Strong technical fluency across:
•Compute: GPU servers, bare metal, provisioning, firmware, hardware validation
•Networking: high-performance fabrics, RDMA, InfiniBand/Ethernet, topology
•Storage: distributed storage, throughput, latency, data paths
•Orchestration: Kubernetes, Slurm, containers, schedulers
•Automation: infrastructure-as-code, configuration management, workflow engines
•Observability: telemetry, health checks, diagnostics, performance monitoring
•Familiarity with benchmarking and validation tools such as NCCL tests, HPL, MLPerf, GPU diagnostics, network and storage benchmarks, or similar.
•Strong understanding of failure handling, repeatability, configuration drift, acceptance testing, and operational automation.
•Able to work across hardware, software, infrastructure, and operations teams to drive a single product outcome.
•Strong prioritisation, communication, and stakeholder management skills.
•Cluster systems thinking: understand how compute, GPUs, networking, storage, software, and orchestration interact as a complete system.
•Product mindset: turn manual deployment and acceptance processes into repeatable platform capabilities.
•Technical fluency: work credibly with engineering teams across hardware and software layers.
•Operational discipline: care about repeatability, quality gates, validation, and measurable readiness.
•Automation mindset: look for opportunities to eliminate manual steps and reduce deployment variance.
•Ownership: drive clusters from onboarding through acceptance, production readiness, and continuous improvement.
Benefits
•25 days of annual leave
•A culture that emphasises results over hierarchy, process & ego
•Open communication and regular feedback
•Dedicated Learning Time for new skills, projects or interests outside day-to-day work
•Private medical insurance via Bupa
•Cycle to Work Scheme
•Gympass subscription to a variety of gyms and wellbeing apps
•Participation in the company shares program
•Enhanced parental pay & leave
