Product Manager, Managed Services

Responsibilities

• Own the product roadmap for managed SLURM and Kubernetes offerings, including control plane architecture, autoscaling, multi-tenancy, and cluster lifecycle management.
• Define requirements for control plane performance, reliability, and availability—including API rate limits, etcd scaling, provisioning tiers, and failure recovery mechanisms.
• Work with engineering to design automated provisioning workflows, health monitoring systems, and node lifecycle controllers that minimize cluster downtime and maximize GPU utilization.
• Partner with datacenter and networking teams to ensure control plane infrastructure scales seamlessly across geographic regions and supports hybrid deployment models.
• Drive decisions on when to build vs. integrate with ecosystem tools (Rancher, OpenShift, Slurm accounting, workload orchestrators) based on customer requirements and competitive positioning.
• Define metrics and SLAs for control plane uptime, API performance, scheduler throughput, and pod/job launch latency.
• Conduct customer discovery to understand pain points around cluster management, job queueing, resource allocation, and multi-cluster orchestration.
• Create product documentation, deployment guides, and reference architectures for enterprise customers running large-scale AI training and inference workloads.
• Analyze competitive offerings from AWS EKS, Google GKE, DigitalOcean DOKS, and specialized HPC providers to inform feature prioritization and pricing strategy.

Requirements

• 5+ years product management experience with at least 3 years focused on infrastructure, platform, or cloud services.
• Deep technical understanding of Kubernetes control plane architecture (kube-apiserver, etcd, scheduler, controller-manager) and SLURM job scheduling.
• Experience building or managing infrastructure products that serve technical users (platform engineers, ML engineers, researchers).
• Track record of shipping features that improved cluster reliability, reduced time-to-deployment, or increased resource efficiency at scale.
• Strong grasp of distributed systems concepts: consensus protocols, failure modes, backpressure handling, and operational complexity tradeoffs.
• Familiarity with GPU workload patterns (multi-node training, inference serving, batch processing) and how control plane design affects performance.
• Ability to synthesize customer feedback, operational data, and competitive intelligence into clear product requirements and technical specifications.
• Experience working with engineering teams to debug production incidents, analyze root causes, and translate findings into product improvements.
• Comfortable navigating ambiguity and making pragmatic tradeoffs between feature completeness, time-to-market, and technical debt.

Nice-to-Haves

• Experience with HPC schedulers (LSF, PBS, Grid Engine), cloud-native storage (Ceph, Lustre), or datacenter automation.

Compensation

• Base salary range: $180,000 - $250,000.
• Package includes base salary, equity, benefits, and for applicable roles, commission plans.
• Final offers vary based on geography, candidate experience, relevant credentials, and other factors.
• Outstanding candidates may be eligible for adjusted terms plus meaningful equity.