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
Linux @ 4
Python @ 4
Algorithms @ 4
Data Structures @ 4
Bash @ 4
Communication @ 4
Rust @ 7
PyTorch @ 4
CUDA @ 4
GPU @ 4
Deep Learning @ 7
AI @ 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
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern deep learning — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
Responsibilities
- Work on first solutions in the industry that bring exceptional performance and security improvements to the infrastructure used by leading applications.
- Develop new features and enable various technologies around data storage for GPU I/O.
- Develop advanced C++/CUDA libraries and algorithms for speed-of-light performance.
- Remove performance bottlenecks by proposing and implementing optimizations in the I/O stack, frameworks, and applications.
- Collaborate with research teams and other specialists; take on complex engineering tasks that advance team and company goals.
Requirements
- Good knowledge of Linux kernel internals, filesystems, object storage systems, databases, and vector databases.
- Good understanding of NVMe and related technologies.
- Development experience in cloud, virtualization (VMware, KVM), and container technologies.
- Advanced knowledge in computer architecture.
- Solid understanding of data structures and algorithms.
- Bash and Python experience.
- Excellent communication and planning skills.
- BS, MS, or PhD in computer science or a related field, or equivalent experience.
- 7+ years of strong coding experience using C, C++, Rust, and Python.
Ways to Stand Out
- Development experience in storage software such as key-value stores, file systems, object storage systems, and vector databases.
- Knowledge of internals of frameworks like PyTorch and JAX.
- Exceptional CUDA programming skills and exceptional C++ programming skills.
Compensation & Benefits
- Base salary range: 184,000 USD - 287,500 USD (determined based on location, experience, and pay of employees in similar positions).
- Eligible for equity and company benefits (link to NVIDIA benefits provided in original posting).
Additional information
- Applications for this job will be accepted at least until March 22, 2026.
- This posting is for an existing vacancy.
- NVIDIA uses AI tools in its recruiting processes.
- NVIDIA is an equal opportunity employer and is committed to fostering a diverse work environment.