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CoreWeave

Rents GPU cloud computing for AI developers

Principal Engineer - Manufacturing Analytics

Data Management +1Confirmed live in the last 24 hours
Full-timeDublin, IrelandIn-person

About the Job

Requirements

  • Have 10 or more years of experience in hardware quality, reliability engineering, manufacturing analytics, or a related field, with demonstrated Principal- or Staff-level scope.
  • Have deep expertise in reliability modeling and statistical methods, including Weibull analysis, mean time between failures/mean time to failure (MTBF/MTTF), accelerated life testing, and defects per million parts (DPPM) and yield analysis.
  • Have a track record of building or architecting manufacturing and quality analytics platforms, rather than only using existing business intelligence tools.
  • Have experience working directly with original equipment manufacturers (OxMs) and component suppliers on data integration and quality data contracts.
  • Have a strong understanding of product lifecycle management concepts, including new product introduction (NPI), engineering change requests/orders (ECR/ECO), sustaining, and end of life (EOL), and how data should inform lifecycle gates.
  • Be comfortable defining strategy, data models, and roadmaps and influencing others to build systems that are not yet fully established.
  • Have excellent written and verbal communication skills and be able to explain technical detail to executive-level audiences.
  • The position requires access to export-controlled information; applicants must meet an applicable U.S. person, export authorization exemption, or export authorization eligibility pathway.

Responsibilities

  • Define and drive the strategy for capturing, standardizing, and integrating quality and process data from OxMs and component suppliers.
  • Establish data contracts, quality gates, and supplier integration patterns to ensure timely, trustworthy, and actionable data flows.
  • Architect an end-to-end analytics strategy spanning supplier, factory-floor, and field data, moving Hardware Quality from siloed reporting to a unified quality and reliability data platform.
  • Enable the operating framework and modeling that connects supplier, manufacturer, and fleet insights into a unified, collaborative solution.
  • Create scalable solutions and set technical direction in partnership with Supply Chain Transformation and Data Enablement teams.
  • Own the technical approach to reliability modeling across hardware components and systems, including failure-rate prediction, MTBF/MTTF analysis, Weibull analysis, and other lifetime distributions.
  • Translate field and factory failure data into predictive reliability models that inform design margin, burn-in strategy, warranty planning, and spares planning.
  • Define how quality, reliability, and supplier data inform product lifecycle decisions, from NPI readiness criteria through sustaining engineering, end of life, and cold-spares planning.
  • Partner with product lifecycle management (PLM), engineering, and program management to embed data-driven gates and reliability checkpoints into lifecycle-stage transitions.
  • Serve as a senior technical voice for Hardware Quality, setting direction for engineers and analysts.
  • Communicate technical strategy to executive stakeholders, framing quality and reliability work in terms of business impact, including revenue protection, customer satisfaction, and cost avoidance.
  • Mentor senior- and staff-level engineers and raise the technical bar for data and reliability work across the organization.

Desired Qualifications

  • Have experience in high-density compute, GPU, or data center hardware environments.
  • Be familiar with product lifecycle management (PLM) platforms.
  • Have a background in statistical software and tooling, including Minitab, JMP, or Python/R for reliability analysis.
  • Have experience with cold-spares planning or hub-and-spoke supply chain models.

About the Company

CoreWeave

Rents GPU cloud computing for AI developers

CoreWeave rents cloud computing to AI labs and businesses that train models and run them in production for research and commercial applications. Its platform combines NVIDIA graphics chips, storage, networking, and software built for demanding AI workloads across connected clusters scaled to fit each project. CoreWeave Forge connects tools for tracking experiments, testing models, and improving AI systems using feedback from production. Customers rent computing capacity through committed contracts or usage charges, while managed services handle the infrastructure needed to keep large workloads running. CoreWeave was founded in 2017 and is headquartered in Livingston, New Jersey.

Company size
1,001-5,000
Company stage
IPO
Headquarters
Livingston, NJ
Founded
2017

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