Technical Program Manager - Performance & Benchmarking

CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at www.coreweave.com .

What You'll Do:

The AI/ML TPM team owns delivery and execution across CoreWeave's AI/ML Platform Services organization. The team partners closely with Product, Engineering, Research, Infrastructure, and Go-to-Market teams to deliver scalable, reliable, and high-performance platforms that support the full AI lifecycle. AI/ML TPMs drive alignment and execution across highly technical, cross-functional teams to ensure the successful delivery of customer-facing infrastructure and platform capabilities used by researchers, engineers, and enterprise customers.

As a Technical Program Manager, you will lead complex, cross-functional programs across Performance Benchmarking within our AI/ML Platform Services organization. This team is responsible for ensuring CoreWeave's infrastructure is performant, stable, and validated for demanding AI workloads before and as it reaches customers. The work spans infrastructure verification, benchmarking, observability, and performance readiness across new hardware platforms, clusters, and model workloads. You will partner with engineering, infrastructure, product, capacity, and go-to-market teams to drive programs that improve workload performance, validate new environments, operationalize benchmarking frameworks, and create visibility into how CoreWeave systems perform across models, hardware generations, and deployment contexts.

In this role, you will:

Drive end-to-end program execution for performance and benchmarking initiatives spanning infrastructure validation, performance testing, benchmark execution, observability, and launch readiness

Partner with engineering and infrastructure teams to deliver programs that verify new hardware platforms, clusters, and software environments meet CoreWeave standards for performance and stability

Lead cross-functional efforts to operationalize benchmarking frameworks that measure model performance, runtime efficiency, GPU utilization, and workload reliability across environments

Coordinate dependencies across platform engineering, infrastructure, capacity, product, and go-to-market teams to ensure performance findings are translated into roadmap priorities, customer readiness, and external proof points

Build program mechanisms for release readiness, benchmark planning, risk management, issue escalation, and post-launch review for performance-sensitive infrastructure initiatives

Establish dashboards, operating cadences, and success metrics to improve performance visibility, infrastructure validation coverage, benchmark repeatability, and time-to-readiness for new platforms

Help drive prioritization across performance bottlenecks, test gaps, and benchmark requests by aligning stakeholders on goals, tradeoffs, and measurable outcomes

Create clarity across ambiguous technical programs by aligning teams around performance goals, validation criteria, and execution milestones

Who You Are:

Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience

5+ years of technical program management experience in cloud infrastructure, distributed systems, high-performance computing, or AI/ML platforms

Experience leading large-scale cross-functional programs involving performance engineering, benchmarking, validation systems, or infrastructure readiness

Strong technical fluency in distributed systems, GPU or accelerator-based infrastructure, workload performance measurement, and large-scale infrastructure operations

Demonstrated ability to define program metrics and drive measurable outcomes in performance, reliability, scale, or operational maturity

Excellent communication skills, with experience influencing engineering, product, and infrastructure stakeholders

Experience with AI/ML benchmarking, performance analysis, or infrastructure validation for training and inference workloads

Familiarity with GPU cluster architecture, workload observability, hardware bring-up, and performance bottleneck analysis

Understanding of benchmarking methodologies, reproducibility, test coverage, and the tradeoffs between performance, stability, utilization, and customer readiness

Experience building launch processes, release governance, dependency management, and operational review mechanisms in fast-scaling environments

Familiarity with translating technical performance data into actionable decisions for product, customer, or go-to-market audiences

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