Capacity Engineer - AI Infrastructure

Position Overview

At eBay, we're more than a global ecommerce leader — we’re changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We’re committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts. Our customers are our compass, authenticity thrives, bold ideas are welcome, and everyone can bring their unique selves to work — every day. We're in this together, sustaining the future of our customers, our company, and our planet. Join a team of passionate thinkers, innovators, and dreamers — and help us connect people and build communities to create economic opportunity for all.

We are seeking a motivated Capacity Engineer to join our central capacity engineering team for eBay's Marketplaces Core Technology organization supporting our artificial intelligence infrastructure. As a Capacity Engineer, you will be responsible for analyzing, modeling, and forecasting the infrastructure needs of our organization. Your expertise in capacity engineering and forecasting will play a crucial role in optimizing resource allocation and ensuring efficient utilization of our technology assets. Experience with public and private Kubernetes based clouds, application performance principles, and workload optimization will be highly helpful in this role. The successful candidate will have strong analytical skills, superb communication abilities, and a deep understanding of technology infrastructure.

Responsibilities

• Collaborate with Core AI teams across multiple geographies to analyze, plan, and execute on AI initiatives.
• Analyze AI capacity intake requirements for prioritization and scheduling.
• Seek out and lead execution of performance optimizations of our AI related assets to ensure efficient use. Scope includes Nvidia SuperPod, On-Prem Kubernetes, Azure, and GCP based clouds.
• Understand key performance metrics and scaling characteristics of LLM, non-LLM AI models.
• Understand key concepts and sizing metrics related to RAG, Vector Search, Grounding.
• Influence customer choice of AI models to improve ROI and cost efficiency.
• Design and build dashboards to support management of AI workloads and infrastructure.
• Be familiar with GPU relevant metrics and how they are used.
• Strong experience with Grafana, Prometheus, Thanos, and ELK stacks.
• Analyze historical data, trends, and growth patterns to develop accurate capacity models such as compute, network, storage, and platform optimization requirements and forecasts.
• Collaborate with multi-functional teams to gather relevant information on business objectives, technology requirements, and upcoming projects.
• Evaluate and refine existing capacity engineering processes and methodologies to improve accuracy and efficiency.
• Monitor system performance metrics and utilization levels to identify potential bottlenecks or areas of underutilization. Take ownership and drive for the realization of gains from improving utilization.
• Collaborate with technology partners to understand future technology trends and initiatives, anticipate resource demands, and develop proactive capacity plans.
• Conduct "what-if" scenarios to assess the impact of different business scenarios and help guide decision-making processes.
• Manage capacity for large federated Kubernetes environments on primarily private cloud but including some public cloud.
• Must be articulate and be able to communicate capacity insights, recommendations, and performance metrics to key partners.
• Advocate for initiatives that provide clear business value through data.

Qualifications

• Bachelor's degree in Computer Science, Information Systems, Statistics, or a related field and 2+ years of experience.
• 2 years of experience in capacity engineering, resource allocation, and forecasting in a technology-intensive environment.
• Specialized experience in AI.
• Strong proficiency with Grafana, Prometheus, Thanos, and ELK stacks.
• Experience with public and private Kubernetes based clouds.
• Understanding of application performance principles and workload optimization.
• Strong analytical skills and superb communication abilities.
• Deep understanding of technology infrastructure.

Employment Type

Full time

Location Type

Information not provided.

Salary

Information not provided.