Top Tech Jobs & Startup Jobs

Create developer-facing technical content for NVIDIA's AI platform: test and evaluate training/inference features, write blogs and guides, build code samples and demos, benchmark multi-GPU systems, engage the developer community, and feed product feedback to engineering and product teams.

Technical lead accountable for end-to-end chip integrity on a major silicon program. Guide feature integration, resolve multi-functional post-silicon bugs (HBM, power, I/O, packaging), steward qualification playbooks, surface risks and decision-ready options for executives, mentor other chip leads, and run critical debug and technical execution forums.

Partner with customers across the DGX Cloud lifecycle to drive adoption and performance. Translate workload and business requirements into scalable compute, networking, storage, and cloud recommendations. Create reusable playbooks, tooling, and dashboards. Lead architecture reviews, performance tuning, cloud portability, and GPU resource planning. Use usage data to identify risks and opportunities and deliver lightweight automation to accelerate delivery and operations.

Lead performance characterization and optimization of large-scale AI workloads across GPUs, networking, storage, and software. Design and run benchmarks and profiling, diagnose bottlenecks, define evaluation methodologies and metrics, produce actionable optimization plans, and collaborate with DL engineers, platform teams, and GPU architects to deliver improvements.

Lead end-to-end program management for NVIDIA Metropolis Vision AI products: drive roadmap execution, coordinate engineering and product teams, manage sprints and KPIs, use Jira/Confluence for reporting, and support model training, optimization, and edge deployment into production.

Own global channel strategy and execution for NVIDIA Jetson and IGX platforms. Manage distributor relationships, drive partner onboarding, pipeline creation, revenue, and partner readiness. Collaborate with marketing on channel programs, MDF-funded activities, demand generation, and performance measurement through reviews, scorecards, and dashboards.

Develop and formalize methods to validate reported vulnerabilities and generate/verify safe patches for internal NVIDIA targets. Define correctness, regression, and revalidation standards; produce repeatable, auditable processes and evidence. Apply AI-assisted workflows cautiously, perform bounded applied research on vulnerability classes, and document findings for independent security review.

Design, implement and verify boot firmware for flagship CPU SoCs. Define and decompose functional and safety requirements, perform FMEA/DFA, validate on pre-silicon simulation, support silicon bring-up and productization, and collaborate across teams to meet safety and security standards.

Apply LLMs, agentic systems, and reinforcement learning to front-end ASIC problems (RTL generation, verification, PPA). Build data generation, evaluation, and production ML tooling; integrate coding agents into EDA/validation flows; collaborate with model teams to fine-tune domain-specific models and deliver measurable speedups to ASIC schedules.

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.