Senior Engineering Manager, Object Storage - DGX Cloud

Role overview Lead an engineering team responsible for a large-scale, S3-compatible object storage service supporting data-intensive AI workloads. The role spans roadmap planning, software development, and production operations, with accountability for service reliability, performance, and team growth.

Responsibilities - Set technical direction and deliver a multi-quarter roadmap in partnership with internal customers and product and architecture teams. - Guide development and operation of storage services, addressing throughput, durability, availability, and scaling needs. - Coordinate with data movement, compute, networking, security, and reliability teams to reduce I/O bottlenecks and keep services running around the clock. - Establish service objectives, capacity plans, incident response practices, and standards for testing, deployment, and observability. - Recruit, mentor, and retain engineers; support career development and build an inclusive team. - Communicate progress, risks, and resource needs to senior stakeholders; encourage effective use of AI-assisted development tools.

Requirements - Degree in computer science, electrical engineering, or a related field, or equivalent experience. - 10+ years in software engineering, including 4+ years managing teams of 10 or more engineers delivering production services. - Deep experience with distributed storage, object storage, or large-scale cloud data services, including hands-on experience with S3-compatible systems in production. - Development experience in Go, C++, Python, or a comparable systems language. - Track record of delivering software across concurrent workstreams and managing scope, risk, and timelines. - Experience with CI/CD, automated testing, reliability objectives, observability, incident management, and developing engineering talent.

Nice to have - Experience operating internal cloud storage services with defined service levels, usage metering, or customer-facing APIs. - Familiarity with AI infrastructure patterns such as checkpoint storage, dataset versioning, or storage-aware scheduling at large GPU scale. - Background in reliability engineering, capacity planning, cost optimization, or chargeback models. - Examples of using AI-assisted development tools to improve engineering productivity.

Benefits and work setup - The listing gives a base salary range of $272,000–$431,250, with equity and benefits eligibility.