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.
Prometheus @ 4
Python @ 6
Distributed Systems @ 4
TensorFlow @ 4
Communication @ 4
Debugging @ 7
API @ 4
PyTorch @ 4
Deep Learning @ 4
Observability @ 4
AI @ 4
NCCL @ 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
Joining NVIDIA's DGX Cloud AI Efficiency Team means contributing to the infrastructure that powers our innovative AI research. This team focuses on developing tools for optimizing efficiency and resiliency of AI workloads - pre-training, post-training, inference. Our objective is to deliver a stable, scalable environment for AI researchers, providing them with the necessary resources and scale to foster innovation.
As a senior DGX Cloud AI Infrastructure software engineer at NVIDIA, you will design, build, and maintain AI infrastructure that enable large-scale AI training and inferencing. You will implement software and systems engineering practices to ensure high efficiency and availability of AI systems and contribute to a culture of blameless postmortems, iterative improvement, and risk-taking.
Responsibilities
- Develop infrastructure software and tools for large-scale pre-training, post-training, and inference.
- Develop and optimize tools and libraries to improve infrastructure efficiency and resiliency.
- Co-design and implement APIs for integration with NVIDIA's resiliency stacks.
- Enhance infrastructure and products underpinning NVIDIA's AI platforms.
- Define meaningful and actionable reliability metrics to track and improve system and service reliability.
- Perform problem-solving, root cause analysis, and optimization.
- Root cause, analyze, and triage failures from the application level to the hardware level.
Requirements
- Minimum of 8+ years of experience in developing software infrastructure for large scale AI systems.
- Bachelor's degree or higher in Computer Science or a related technical field (or equivalent experience).
- Strong debugging skills and experience in analyzing and triaging AI applications from the application level to the hardware level.
- Experience with observability platforms for monitoring and logging (e.g., ELK, Prometheus, Loki).
- Proven track record in building and scaling large-scale distributed systems.
- Experience with AI training and inferencing infrastructure services.
- Proficiency in programming languages such as Python, C/C++, and scripting languages.
- Experience in quality software engineering practices, including test development, defensive programming, version control, and CI.
- Excellent communication and collaboration skills; a culture of diversity, intellectual curiosity, problem solving, and openness are essential.
Ways to stand out
- Background in working with large scale clusters.
- Experience in defining and building observability and telemetry software stacks.
- Experience with RDMA software stack (NCCL, IB verbs, UCX, libfabrics).
- Experience and root cause analysis of failures at datacenter scale.
- Good understanding of deep learning frameworks internals: PyTorch, TensorFlow, JAX, and Ray.
Compensation & Benefits
- Base salary range: 184,000 USD - 287,500 USD for Level 4; 224,000 USD - 356,500 USD for Level 5.
- You will also be eligible for equity and benefits.
Additional information
- Applications for this job will be accepted at least until February 16, 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.