Job description

NVIDIA's Object Storage Platform team builds and operates the company's internal S3-compatible distributed object storage service — a critical piece of infrastructure that stores, manages, and serves exabytes of data across NVIDIA's on-premises and hybrid environments. This platform is the storage backbone for NVIDIA's AI infrastructure, enabling researchers and engineers to reliably store massive datasets, model checkpoints, and training artifacts at scale. A companion data movement team extends the platform with tooling that efficiently stages and moves data closer to GPU clusters, minimizing idle accelerator time and accelerating training and inference pipelines. We are seeking a seasoned Engineering Manager to lead this organization across two closely aligned teams: the core Object Storage platform team and the Data Movement Tools team. You will own the full software development and service delivery lifecycle — from roadmap planning through production operations — while building a high-performance engineering culture grounded in technical excellence, service reliability, and continuous delivery. What You'll Be Doing: • Lead and grow a multi-team engineering organization, setting a high bar for software quality, service reliability, and engineering culture. • Own roadmap execution for NVIDIA's internal object storage service — partnering with internal customers, Product Management, and Architecture to translate multi-quarter goals into clear engineering plans with measurable milestones. • Drive development and operation of NVIDIA's S3-compatible object storage service, ensuring it meets the performance, durability, availability, and scalability demands of AI training and inference workloads at exabyte scale. • Lead the Data Movement Tools team in building and evolving tooling that stages datasets, model checkpoints, and artifacts from distributed storage to GPU-adjacent compute — minimizing I/O bottlenecks and keeping accelerators fully utilized. • Define and uphold service reliability standards: SLOs, capacity planning, incident response, root cause analysis, and on-call hygiene. Partner with SRE to ensure the platform meets the availability commitments internal customers depend on. • Establish and enforce engineering standards across both teams: design reviews, code quality, CI/CD practices, automated testing, and production observability. Recruit, mentor, and develop engineers across all levels, conducting regular 1:1s, performance cycles, and career growth conversations. Build a diverse, inclusive, and high-retention team. • Collaborate closely with SRE, Platform, Networking, and Security teams to ensure smooth transitions from development to production and rapid resolution of customer-impacting issues. • Champion the adoption of AI-assisted development tooling — coding assistants, agentic workflows, and automated testing harnesses — to accelerate team productivity and raise engineering output. Represent the Object Storage engineering organization to senior leadership, providing transparent status updates, surfacing risks early, and advocating for the resources needed to succeed. What We Need to See: • BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field — or equivalent experience. • 10+ overall years of software engineering experience, including 4+ years in an engineering management role leading teams of 10 or more engineers delivering production services at scale. • Deep technical background in distributed storage systems, object storage platforms, or large-scale cloud data services; hands-on development experience in Go, C++, Python, or equivalent systems languages. • Direct, hands-on experience building or scaling S3-compatible object storage systems in a production cloud or private cloud environment — with demonstrable improvements in throughput, durability, or operational efficiency. • Demonstrated experience building or operating cloud storage services — with accountability for reliability, performance, and capacity at scale in a production environment. • Proven track record of shipping production software on time — managing scope, risk, and delivery across multiple concurrent workstreams. • Strong experience with modern software development and service delivery practices: CI/CD, automated testing, SLO-based reliability, production observability, and incident management. • Demonstrated ability to attract, develop, and retain strong engineering talent in a driven environment, with a track record of growing engineers into senior and staff-level roles. • Excellent written and verbal communication — able to translate complex technical trade-offs for product partners and engineering constraints for executive audiences. Ways to Stand Out from the crowd: • Prior experience designing and operating internal cloud storage services (IaaS/PaaS) with well-defined SLAs, metered usage, and internal customer-facing APIs. • Background in data movement, data staging, or prefetching tooling for AI/ML workloads — with direct experience optimizing data pipelines to reduce GPU idle time during training or inference. • Familiarity with AI infrastructure storage patterns: checkpoint storage, dataset versioning, write-once-read-many (WORM) access patterns, or storage-aware scheduling at 10k+ GPU scale. Experience managing capacity planning, cost optimization, and chargeback modeling for shared internal storage infrastructure. • Track record of adopting AI-assisted development tools to meaningfully improve team productivity, with concrete examples. History of growing engineers into senior ICs or leads, and building diverse, inclusive teams with strong retention. NVIDIA's Object Storage Platform and data movement tooling form a critical layer in keeping NVIDIA's GPU fleet productive — every model trained, every checkpoint saved, and every dataset staged passes through the systems this team builds and operates. This is a high-impact role at the center of NVIDIA's AI infrastructure. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD for Level 4, and 320,000 USD - 488,750 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until August 31, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

Lead and grow a multi-team engineering organization, setting a high bar for software quality, service reliability, and engineering culture.
Collaborate closely with SRE, Platform, Networking, and Security teams to ensure smooth transitions from development to production and rapid resolution of customer-impacting issues.
BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field — or equivalent experience.
10+ overall years of software engineering experience, including 4+ years in an engineering management role leading teams of 10 or more engineers delivering production services at scale.
Deep technical background in distributed storage systems, object storage platforms, or large-scale cloud data services; hands-on development experience in Go, C++, Python, or equivalent systems languages.
Demonstrated experience building or operating cloud storage services — with accountability for reliability, performance, and capacity at scale in a production environment.
Proven track record of shipping production software on time — managing scope, risk, and delivery across multiple concurrent workstreams.
Strong experience with modern software development and service delivery practices: CI/CD, automated testing, SLO-based reliability, production observability, and incident management.
Demonstrated ability to attract, develop, and retain strong engineering talent in a driven environment, with a track record of growing engineers into senior and staff-level roles.
Excellent written and verbal communication — able to translate complex technical trade-offs for product partners and engineering constraints for executive audiences.
Prior experience designing and operating internal cloud storage services (IaaS/PaaS) with well-defined SLAs, metered usage, and internal customer-facing APIs.
Background in data movement, data staging, or prefetching tooling for AI/ML workloads — with direct experience optimizing data pipelines to reduce GPU idle time during training or inference.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until August 31, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aici/cdgopythonml

Platform: eightfold
Effort: medium