Top Tech Jobs & Startup Jobs

Lead and build a system administration team to operate and automate a software cloud lab, manage hardware inventory, optimize power/space/cost, ensure timely setup and maintenance, and enable automated regression and development workflows.

Perform data-driven failure and yield analysis for high-speed transceiver products. Lead task forces, design and improve factory test plans, build automation platforms, manage failure analysis, collaborate across Process/Mechanical/Hardware/Thermal/Quality teams, and drive product performance and yield improvements.

Lead product strategy, roadmap, and go-to-market for NVIDIA enterprise game development solutions. Partner with engineering, ISVs, OEMs, and sales to integrate RTX, generative AI, and developer workflows into studio pipelines. Drive positioning, demos, trade-show strategy, sales enablement, and lighthouse case studies to grow adoption among game developers and publishers.

Benchmark and analyze DL/ML workloads across GPU/NPU architectures; build and validate performance models and projections for LLM/GenAI; identify bottlenecks and propose hardware/software optimizations; evaluate new capabilities and use AI agents to accelerate performance investigations.

Define vision and operational strategy for developer marketing; build acquisition, education, community, and advocacy programs; drive presence across open-source hubs; create partner co-marketing; evolve NVIDIA Deep Learning Institute; support higher education/research; translate developer signals into product and business priorities; and hire and lead a high-performing developer marketing organization.

Design and implement firmware for NVIDIA Spectrum Ethernet switches, collaborating with hardware and software teams to develop, debug, and integrate real-time embedded features and performance-improving algorithms.

Lead software and system architecture for next-generation DPU/SmartNIC management, QoS, telemetry, and observability. Define end-to-end control flows, interfaces, APIs, telemetry requirements, and architecture specs. Collaborate with hardware, firmware, drivers, validation, product, and customers through pre-silicon design, bring-up, and production readiness to ensure performance, diagnosability, and integration in large-scale AI and cloud datacenter environments.

Design and implement physical chip builds (RTL-to-GDS) including synthesis, P&R, timing closure, power and noise analysis, power/clock distribution, and physical verification. Resolve timing and congestion, improve flows, and collaborate with engineering teams to integrate efficient, low-latency networking silicon.

Lead performance strategy, characterization, and optimization for large-scale NVIDIA AI GPU clusters. Focus on RDMA, collective communications (NCCL/MPI), congestion control, load balancing, DPUs, storage for inference, and cluster observability via telemetry and Grafana dashboards. Perform deep RCA and coordinate mitigation across hardware, firmware, and software teams.

Lead technical and operational programs for enterprise AI partners. Define schedules, translate customer requirements, drive cross-functional execution from design to production, manage risks and post-deployment support, and coordinate global teams to integrate complex AI model stacks into customer environments.