Senior Manager, Capacity Engineering

. Lead the Capacity Engineering team and establish its 12–18 month functional and technical strategy, roadmap and success measures .

Posted 9/25/2026full-timeSan Francisco • California • United StatesSenior💰 $208,592 - $429,454 per yearWebsite

Tools & technologies

CloudDistributed SystemsKubernetes

Key responsibilities & impact

Lead the Capacity Engineering team and establish its 12–18 month functional and technical strategy, roadmap and success measures
Develop CPU and GPU forecasts and supply plans accounting for workload demand, delivery constraints, cost and reliability requirements
Guide capacity requests, reservations, entitlements, allocation policy and infrastructure-data systems
Improve utilization and efficiency across Kubernetes-based shared compute platforms, GPU infrastructure and other cloud environments
Establish metrics and operating practices for capacity risk, allocation, infrastructure cost, realized savings and system health
Partner with Infrastructure, SRE, Finance, product and platform teams and cloud providers on capacity and infrastructure-investment decisions
Plan team structure and headcount, recruit senior talent, develop technical leaders and sustain an inclusive, accountable, high-performing team
Use AI to accelerate capacity analysis, forecasting, operational investigations and repeatable workflows while applying judgment and verification

What you’ll need

Bachelor’s degree in computer science, a related field or equivalent experience
Experience managing and developing teams building or operating large-scale cloud infrastructure, distributed systems, compute platforms or resource-management systems
Experience defining and delivering a 12–18 month functional and technical strategy through senior technical leaders across a broad infrastructure portfolio
Technical experience in public-cloud infrastructure, highly available distributed systems, Kubernetes or shared-compute platforms and capacity-management systems
Experience leading cross-functional infrastructure programs and communicating availability, cost and capacity tradeoffs to Engineering, Finance, SRE and cloud-provider stakeholders
Demonstrated ability to use AI to improve speed and quality in day-to-day workflow
Critical evaluation and verification of AI-assisted work, including testing, source-checking, data validation and peer review
High integrity and ownership, including protecting sensitive data, avoiding over-reliance on AI and remaining accountable for final decisions and deliverables

Comp & perks