In this role, you will collaborate across infrastructure, platform software, and multi-functional engineering teams to transform technical direction into actionable plans, clear tradeoffs, and coordinated progress.

Responsibilities

• Work together with infrastructure and platform leaders to drive forward strategic technical programs across cloud infrastructure and platform software.
• Synthesize inputs from architecture, operations, engineering, and partner teams into clear program direction and actionable plans.
• Develop strategy briefs, roadmap proposals, dependency maps, and phased execution plans for multi-team initiatives.
• Drive alignment on technical tradeoffs, sequencing, priorities, and implementation approaches across organizations.
• Help translate platform evolution into scalable processes, operating models, and delivery mechanisms.
• Identify recurring structural gaps and recommend durable improvements across planning, governance, and execution.
• Connect long-term planning with delivery achievements, readiness criteria, and measurable outcomes.
• Facilitate leadership decisions by providing clear options, risks, and recommendations.
• Represent the coordinated program view across technical and business collaborators.

Requirements

• 12+ years of experience leading technical programs in infrastructure, platform software, systems, or comparable technical domains.
• Proven experience leading multi-functional strategic programs from concept through execution.
• Strong ability to work with deeply technical teams and turn complexity into clear decisions and plans.
• Excellent analytical, written, and verbal communication skills.
• Demonstrated success influencing direction across multiple teams without direct authority.
• Technical proficiency in cloud platforms, distributed systems, infrastructure software, or systems architecture.
• Bachelor’s degree in Computer Science, Engineering, or equivalent experience.

Nice to Have

• Experience spanning multiple infrastructure domains such as networking, storage, observability, security, or platform operations.
• Experience connecting platform strategy to execution in large-scale technical organizations.
• Familiarity with AI infrastructure, accelerated computing, or cloud platform ecosystems.
• Strong history of converting operational lessons into durable platform improvements.

NVIDIA offers highly competitive salaries and a comprehensive benefits package. The base salary range is 200,000 USD - 322,000 USD. You will also be eligible for equity and benefits.

Job Data by YubHub.

You will work on cluster readiness and certification software, including the open-source Cluster Readiness Engine, that validates GPU clusters across Kubernetes and Slurm, measures real training and inference performance, and produces evidence customers and partners can trust.

Responsibilities

• Shape the Future of Validation: Design and build the systems that turn cluster validation from a manual exercise into repeatable, verifiable software.
• Core Development: Design and implement cluster readiness and certification tooling in Go and Python that runs on Kubernetes and Slurm, including controllers, operators, CLIs, and scheduler integrations.
• Benchmarking & Workloads: Build a catalog of benchmarks and real workloads spanning communication (NCCL), compute, storage, diagnostics, training, inference, reliability, and security, with per-architecture expectations and clear pass/fail criteria.
• Data Provenance: Develop provenance and evidence pipelines that capture what actually ran, where it ran, and under which configuration, and that produce signed, auditable results.
• Platform Integration: Bring new GPU platforms, schedulers, and fabrics under test, from first bring-up on a single node through multi-node validation on production clusters.
• Cross-functional Collaboration: Collaborate with internal platform, networking, and solutions teams and with cloud partners to root-cause performance and correctness gaps found by the tooling, and feed fixes back into platforms and reference architectures.
• Open Source Contribution: Contribute to the open-source Cluster Readiness Engine and related projects, including design documents, documentation, and reviews.
• Technical Authorship: Author design documents, runbooks, and operational guides that codify how clusters are validated and how results are interpreted.

Requirements

• BS, MS, or Ph.D. in Computer Science, Computer/Electrical Engineering, or a related technical field, or equivalent experience.
• 8+ years of software engineering experience building infrastructure or systems software, ideally for large-scale compute, cloud, or HPC environments.
• Strong programming skills in Go and Python, with experience shipping production-quality services, controllers, or command-line tools.
• Deep familiarity with Linux systems, containers, and Kubernetes, including writing operators or controllers against the Kubernetes API.
• Working knowledge of HPC schedulers such as Slurm and of GPU cluster fundamentals: drivers, NCCL, InfiniBand or RoCE fabrics, and GPU health tooling such as DCGM.
• Experience benchmarking or validating distributed systems, with a rigorous approach to measurement, reproducibility, and evidence.
• Demonstrated ability to drive complex technical investigations to root cause and to communicate findings clearly to engineering and leadership audiences.

Nice to Have

• Hands-on experience running or tuning large-scale distributed training or inference workloads with frameworks such as PyTorch, Megatron, NeMo, vLLM, SGLang, or TensorRT-LLM.
• Experience with the NVIDIA ecosystem, including DGX systems, GB200/GB300 platforms, CUDA, NVLink and NVSwitch fabrics, and NVIDIA networking.
• Prior contributions to open-source infrastructure projects, especially Kubernetes operators, scheduler plugins, or benchmarking frameworks.
• Background in software supply-chain security, attestation, or signed artifacts, and in building audit-grade evidence into systems.
• Familiarity with observability stacks (Prometheus, Grafana, OpenTelemetry), GitOps workflows, and infrastructure as code for GPU clusters.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

Job Data by YubHub.

The FCM team designs specialized RAM implementations across NVIDIA's wide array of processing chips. Be it high speed, low power, multiport, we engage closely with processor architecture teams to build custom solutions across the entire NVIDIA silicon portfolio. Are you interested in designing circuits for the next generation of AI chips? Join a team of dedicated engineers developing the custom SRAM circuits that help power these chips.

Responsibilities

• Design best-in-class SRAM circuits using state-of-the-art technology processes
• Optimize circuits for performance, area, and power
• Collaborate with mask designers to craft high quality and dense pitch-matched layout
• Verify functionality, electrical integrity, and robustness
• Improve/develop flows and methodologies to streamline design automation, data collection, and analysis to ensure working silicon

Requirements

• BS/MS/PhD (or equivalent experience) in Electrical or Computer Engineering
• Minimum 6 years of circuit design experience
• Experience with scripting languages - Perl, Python, Shell
• Strong understanding of SRAM and memory design techniques and macro/block development

Preferred Qualifications

• Self-motivation, attention to detail, clear data analysis and presentation skills
• Familiarity with industry tools such as Cadence Virtuoso for schematic and layout, SPICE simulators, waveform viewers
• Background with developing and using various flows and methodologies, including: Static Timing Analysis, EM and IR analysis, Formal Functional Verification

NVIDIA has continuously reinvented itself over two decades. Our 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 AI , the next era of computing. NVIDIA is a “learning machine” that constantly evolves by adapting to new opportunities that are hard to solve, that only we can tackle, and that matter to the world. This is our life’s work, to amplify human imagination and intelligence. Make the choice, join our diverse team today!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 264,500 USD for Level 4, and 196,000 USD - 310,500 USD for Level 5.

You will also be eligible for equity and benefits.

Job Data by YubHub.