2 days ago
Base Salary
$200k - $322k/yr
Responsibilities
- Design and build distributed services and data pipelines for capacity planning, allocation, reservations, and utilization.
- Develop a unified model of available, committed, and forecasted GPU capacity across cloud providers, regions, clusters, and products.
- Automate capacity-management workflows and build APIs, tools, and integrations for capacity-aware decisions.
- Improve forecasting, scenario planning, operational visibility, monitoring, data quality, and service-level indicators.
- Lead technical design reviews, establish engineering standards, mentor engineers, and diagnose complex production issues.
- Improve the reliability, performance, and scalability of capacity-management services.
Requirements
- Bachelor’s degree in Computer Science, Computer Engineering, or a related technical field, or equivalent experience.
- 12+ years of software engineering experience building production systems.
- Strong programming experience in Python, Go, Java, or similar languages.
- Experience designing distributed systems, backend services, APIs, and data-processing pipelines.
- Experience with cloud infrastructure, Kubernetes, compute platforms, or large-scale resource-management systems.
- Strong understanding of data modeling, system integration, observability, and production operations.
- Ability to translate ambiguous business and operational requirements into clear technical designs.
- Strong communication skills and experience working across engineering and non-engineering organizations.
- Preferred experience includes GPU infrastructure, AI/ML platforms, schedulers, cluster management, accelerated computing, capacity planning, inventory, quota, reservations, resource allocation, optimization, forecasting, simulation, operations research, multi-cloud infrastructure, and technical leadership.
Benefits
- Eligible for equity and benefits.
- Base salary is determined by location, experience, and comparable employee compensation.
- Applications will be accepted at least until September 24, 2026.
Tech Stack
Categories
BackendData Engineering
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.