Senior AI Data Platform Engineer - Multi-Cloud, GPU

NVIDIA Corporation; Austin (TX); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Equity

Benefits

Job summary

NVIDIA Corporation is seeking an experienced engineer to design and build a multi-cloud AI data platform for GPU workloads. You will tackle data placement, caching, streaming, checkpointing, and Kubernetes integrations with quotas and lifecycle management.

You will measure and improve data access and reliability using telemetry, capacity planning, and incident response processes to prevent storage-related failures.

Qualifications

• Requires a bachelor’s or master’s degree in a relevant engineering or computer science field, or equivalent experience.
• At least five years building production systems or data-infrastructure software.
• Deep experience with distributed or object storage or large-scale data pipelines; strong I/O performance knowledge.

Responsibilities

• Design and build a multi-cloud AI data platform for GPU workloads, including data placement, caching, streaming, checkpointing, Kubernetes integrations, quotas, and lifecycle management.
• Measure and improve data access and reliability through telemetry, capacity planning, incident response, and prevention of storage-related failures.
• Collaborate to ensure scalable, robust data infrastructure supporting AI workloads.

Skills

Distributed Storage Systems

Object Storage

Data Pipelines

I/O Performance

Go

Rust

C

C++

Python

Kubernetes

Slurm

AI Training Data

Distributed Caching

Checkpoint Management

Quota and Capacity Management

I/O Telemetry

Education

Bachelor’s or Master’s in Engineering or Computer Science

Tools

Kubernetes

Slurm

Go

Rust

C

C++

Python

Job description

NVIDIA Corporation is seeking an experienced engineer to design and build a multi-cloud AI data platform for GPU workloads. You will tackle data placement, caching, streaming, checkpointing, and Kubernetes integrations with quotas and lifecycle management.

You will measure and improve data access and reliability using telemetry, capacity planning, and incident response processes to prevent storage-related failures.