Data Services Lead - Managed Databases for AI

Coreweave

United States

On-site

USD 207,000 - 275,000

Full time

14 days+
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Benefits offered by this job

Medical, dental, and vision insurance
Company-paid Life Insurance
401(k) with generous match
Flexible PTO
Tuition Reimbursement
ESPP

Job summary

CoreWeave is seeking a senior engineer manager for the Data Service Team to lead the data infrastructure portfolio and drive the strategy for scalable data services. You will hire, mentor, and grow a mixed-skill team, and partner with platform, product, and infrastructure teams to deliver reliable storage capabilities across the stack.

You will shape data access, security, and telemetry-driven improvements while maintaining high performance and resilience for cutting-edge AI workloads in a

Qualifications

  • 5+ years managing data service infra teams.
  • Experience with one or more database internals (MySQL, PostgreSQL, CosmosDB, MongoDB).
  • Hands-on cloud-native and storage/compute separation experience.

Responsibilities

  • Build a world-class engineering team by hiring and developing the best talent.
  • Tackle hard system-level problems to run demanding AI workloads.
  • Design and implement data services via APIs for managed solutions.
  • Improve performance, security, reliability, and scalability of data services.
  • Establish guidelines and data access/storage guard rails for stakeholders.

Skills

Data service infra
Database internals
Kubernetes
Reliability engineering
Leadership
Cloud native architecture
Communication

Tools

APIs
Cloud platforms

Job description

CoreWeave is seeking a senior engineer manager for the Data Service Team to lead the data infrastructure portfolio and drive the strategy for scalable data services. You will hire, mentor, and grow a mixed-skill team, and partner with platform, product, and infrastructure teams to deliver reliable storage capabilities across the stack.

You will shape data access, security, and telemetry-driven improvements while maintaining high performance and resilience for cutting-edge AI workloads in a

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