SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Lambda is a leader in AI cloud infrastructure serving tens of thousands of customers, from AI researchers to enterprises and hyperscalers. The company's mission is to make compute as ubiquitous as electricity and give everyone access to superintelligence.
Lambda has grown 10x over the last three years, and the network engineering organization is scaling to match. We are seeking a Director of Network Capacity Automation to lead one or more teams building the software that enables planning and delivery of network capacity ahead of customer demand. This capacity is critical to ensure a flawless customer connectivity experience to Lambda's GPU cloud infrastructure.
In this role, you will own network capacity end-to-end—planning, delivery, turn-up, and lifecycle—and be accountable for capacity landing ahead of demand. You will build and lead the organization that delivers this: hiring, developing, and retaining engineers and the leaders who manage them, while designing team structure as the organization grows. You will set multi-year technical strategy for capacity automation to ensure it continually scales, converting manual processes into durable software with the goal of a capacity pipeline that runs without heroics.
You will own the operational bar for your organization: how capacity work is planned, reviewed, shipped, measured, and learned from when it goes wrong. You will manage through leads and senior individual contributors, developing both through regular 1:1s, clear performance feedback, growth planning, and sponsorship of meaningful work. You will partner with the Interconnect team on external connectivity negotiations and coordination, work with Principal Engineers and technical leadership to maintain high engineering standards, and collaborate with Infrastructure, Supply Chain, Data Center Operations, Product, and Finance teams to align capacity roadmaps and manage long-lead dependencies.
You will represent your organization's work and technical direction to senior leadership, enterprise customers, and partners, and contribute to organization-wide engineering process improvements.
This role reports to the VP of Cloud and AI Networking and requires presence in the Bellevue or San Francisco office 4 days per week, with Tuesday designated as the work-from-home day.
**Requirements:**
- 8+ years of engineering management experience, including directly managing senior individual contributors
- Proven track record of building and scaling engineering organizations that deliver mission-critical, high-performance cloud connectivity serving millions of users
- Software engineering background with shipped production systems and credible engagement on architecture, technical tradeoffs, and code quality
- Ability to foster a data-driven, automation-first, high-velocity organization
- Familiarity designing, building, scaling, and operating large-scale networks
- Familiarity with building distributed systems and large-scale networking services
- Strong automation skills and track record accelerating business delivery velocity at scale
- Track record of building high-performing teams in fast-moving, technically demanding environments
- Skilled at translating ambiguous business and product goals into clear team priorities and executable engineering plans
- Strong judgment about when to go deep technically, when to delegate, and when to escalate
- Track record of successful project and product delivery in fast-paced, high-pressure environments
- Excellent written and verbal communication skills, leveraging high-quality written artifacts to inform decision-making
**Nice to Have:**
- Experience in GPU cloud, HPC, or AI/ML infrastructure environments
- Deep familiarity with large-scale data center or cloud networking (fabric build-out, capacity modeling, network supply chain)
- Experience with infrastructure capacity forecasting and long-lead hardware planning
- Prior experience at a high-growth infrastructure or cloud company
