Top Tech Jobs & Startup Jobs

Lead NVIDIA’s HPE Enterprise ecosystem growth strategy, driving adoption of accelerated computing across strategic industries. Build executive relationships, develop partnerships, manage pipelines, coordinate global go-to-market programs, support complex sales negotiations, and collaborate with sales, marketing, product, engineering, and channel teams. Shape product direction, demand generation, training, and multi-region initiatives to expand revenue and market leadership.

Verify GPU ASIC hardware at the unit and subsystem levels through directed and random testing, testbench infrastructure, coverage closure, and verification strategy development. Collaborate with RTL, architecture, software, design, and methodology teams to refine verifiable microarchitecture. Support post-silicon validation and use AI for testbench development, automation, debugging, and problem-solving.

Develop pre-silicon performance and system-level models for GPUs and generative AI workloads. Analyze bottlenecks, study GPU microarchitecture and system design tradeoffs, improve model correlation and projection accuracy, and evaluate performance optimizations for LLMs and agentic AI systems. Collaborate with kernel, hardware, and framework teams while using profiling and benchmarking tools to present technical findings.

Build and scale NVIDIA’s AI infrastructure platform for GPU provisioning, configuration, lifecycle management, monitoring, health management, and distributed-system qualification. Develop automation for data center operations and machine learning systems, integrate hardware through AI applications, and work with GPU diagnostics, cluster and network telemetry, Kubernetes or SLURM, Linux, and fault-tolerant distributed architectures.

Design and deploy AI networking platforms in customer data centers. Partner with customers on GPU, compute, storage, and fabric architecture; support infrastructure bring-up; troubleshoot InfiniBand, RoCE, Linux, NIC, and switching issues; conduct technical reviews; build proof-of-concepts; and translate customer requirements into product and engineering feedback. Collaborate with systems engineering, product management, and sales while supporting large-scale AI cluster deployments.

Serve as a technical advisor to NVIDIA Cloud Partners, designing and deploying AI and HPC GPU infrastructure. Build generative AI and LLM proof-of-concepts, support customers throughout the infrastructure lifecycle, analyze performance and scaling, collaborate with sales and technical teams, and deliver customer workshops and presentations.

Leads cross-functional technical programs for NVIDIA OEM partners and AI Factory customers across hardware development, deployment, operations, and support. Identifies ecosystem and service-readiness gaps, coordinates engineering and partner teams, drives debugging and issue resolution, establishes milestones and accountability, and improves deployment and lifecycle-support processes. The role also develops training, standardizes program tracking and reviews, influences leadership decisions, and converts field feedback into repeatable product and operational improvements.

Performs formal verification of RTL units for next-generation CPU and GPU designs. Develops verification plans, test environments, assumptions, assertions, cover properties, and scripts; applies formal techniques to prove digital design correctness; debugs RTL failures; and collaborates with design and verification teams. Requires strong analytical skills, computer architecture and digital logic knowledge, HDL experience, and understanding of temporal logic assertions.

Leads technical engagements with Tier-1 telecommunications customers and partners, mapping workloads to NVIDIA technologies. Designs scalable architectures, integration milestones, and deployment plans; validates solutions; troubleshoots complex issues; enables platform adoption; and communicates customer needs to engineering and product teams. The role requires deep telecom stack expertise, customer-facing technical leadership, and experience with software, hardware, AI inference, networking, containers, Kubernetes, and potentially CUDA/GPU programming.

Manage NVIDIA developer social channels through content planning, scheduling, copywriting, monitoring, analytics, and campaign reporting. Coordinate with engineering, marketing, creative, and communications teams to develop developer-focused social content, videos, event plans, and tutorials. Lead social media projects, pilot new audience opportunities, edit multimedia assets, and measure campaign effectiveness for technical and AI-focused audiences.