Lead Performance Engineer - (10+ years)-11943

Coupa makes margins multiply through its community-generated AI and industry-leading total spend management platform for businesses large and small. Coupa AI is informed by trillions of dollars of direct and indirect spend data across a global network of 10M+ buyers and suppliers. We empower you with the ability to predict, prescribe, and automate smarter, more profitable business decisions to improve operating margins. Why join Coupa? Pioneering Technology: At Coupa, we're at the forefront of innovation, leveraging the latest technology to empower our customers with greater efficiency and visibility in their spend. Collaborative Culture: We value collaboration and teamwork, and our culture is driven by transparency, openness, and a shared commitment to excellence. Global Impact: Join a company where your work has a global, measurable impact on our clients, the business, and each other. Learn more on Life at Coupa blog and hear from our employees about their experiences working at Coupa. We are looking for a highly experienced Senior/Lead Performance Engineer with 12+ years of experience to drive system performance, scalability, and reliability across the Coupa platform. In this role, you will lead performance engineering initiatives for enterprise cloud systems and integrate cutting-edge AI/LLM performance engineering and testing methodologies to optimize both traditional microservices and AI-driven platform capabilities

What You Will Do:

Benchmarking & Certification: Establish performance benchmarks for the Coupa platform and design, implement, and maintain an automated performance certification test harness. AI & Platform Performance Testing: Design load, stress, latency, and concurrency testing suites for AI/LLM workloads (e.g., token generation speed, TTFT—Time to First Token, inference throughput, model server capacity, and retrieval augmented generation/RAG pipeline performance). Troubleshooting & Optimization: Troubleshoot complex configuration, infrastructure, networking, application, and AI/ML model deployment problems impacting system performance or capacity. Cross-Functional Collaboration: Collaborate with Product Management, Development Engineering, AI/ML Engineering, and Cloud Infrastructure teams to determine optimal ways to scale enterprise applications and underlying infrastructure. System Optimization: Provide technical leadership to improve overall system performance, capacity, reliability, cost-efficiency, and scalability (including GPU/CPU compute optimization for AI models). Observability & Monitoring: Monitor and manage production performance using modern tools, continuous automation, and comprehensive AI observability stacks. Proactive Analysis: Conduct proactive end-to-end cluster scalability, performance, and exception analysis across multi-cloud environments. Root Cause & Latency Analysis: Analyze root causes for latency in individual components, database layer, network hops, and AI pipeline dependencies (e.g., lookups, context window processing). Continuous Improvement: Continually evaluate opportunities to modernize our platform, improve process efficiency, and maximize business impact. Thought Leadership: Serve as an evangelist for performance engineering best practices, modern load testing tools, and AI performance frameworks across the organization.