Used Tools & Technologies
Not specified
Required Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Security @ 4
Go @ 4
Kubernetes @ 4
Linux @ 4
Python @ 4
Java @ 4
Algorithms @ 7
Data Structures @ 7
Distributed Systems @ 7
SRE @ 4
Rust @ 4
API @ 4
GPU @ 4
Observability @ 4
AI @ 4
Data Pipelines @ 4
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
The NVIDIA DGXC Data Services team builds cloud-native systems, frameworks, and services for managing data across hybrid and multi-cloud infrastructure. The team is building next-generation data and storage infrastructure for AI: storage, access, ingestion, governance, observability, and data management for exabyte-scale, high-performance GPU-based training and inference jobs. Work enables NVIDIA teams to build, train, deploy, and operate AI products at scale.
Responsibilities
- Build storage technologies, client libraries, and filesystem frameworks that help AI workloads access data across object stores, file systems, and hybrid cloud infrastructure.
- Develop high-performance storage paths for training and inference workflows, including data loading, checkpointing, caching, POSIX-style access, and object-store integration.
- Build observability systems that diagnose storage bottlenecks, attribute GPU idle time to I/O behavior, and expose actionable telemetry through production monitoring stacks.
- Improve performance, scalability, and reliability of storage systems serving massive datasets, deep directory trees, and high-concurrency AI workloads.
- Collaborate with internal AI teams, platform teams, SRE, and operations to validate storage behavior against real workloads and production environments.
- Use modern software engineering practices, including AI-assisted and agentic development workflows, while maintaining standards for design, testing, security, performance, and verification.
Requirements
- BS in Computer Science, Information Systems, Computer Engineering, or equivalent experience, with 5+ years of software engineering experience.
- Strong foundation in algorithms, data structures, distributed systems, operating systems, and practical software design.
- Experience building performance-sensitive systems, storage, backend, or cloud-native software in languages such as Go, Python, Rust, C/C++, or Java.
- Experience with storage systems, object stores, caching, Linux systems, Kubernetes, or cloud infrastructure.
- Ability to reason about performance, scalability, concurrency, reliability, and operational tradeoffs in production systems.
- Ability to design APIs, document systems, communicate clearly, and break ambiguous infrastructure problems into practical execution plans.
- Curiosity and practical judgment around AI-assisted or agentic engineering workflows, including using clear intent, specifications, acceptance criteria, tests, and verification to guide development.
Ways to Stand Out
- Background with Linux kernel observability, eBPF, tracing, or low-overhead telemetry systems.
- Experience with FUSE, POSIX filesystems, object-store-backed filesystems, or filesystem metadata/indexing.
- Experience optimizing storage performance for AI training, checkpointing, inference, or large-scale data pipelines.
Compensation and Benefits
- Base salary range: 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.
- Eligible for equity and benefits (link to NVIDIA benefits).
Additional Information
- Applications for this job will be accepted at least until July 10, 2026.
- This posting is for an existing vacancy.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and committed to an inclusive work environment.