Top Tech Jobs & Startup Jobs

Design and operationalize firmware and software architectures for next-generation NVIDIA Edge AI platforms. Define scalable platform requirements with hardware, software, ISV, and IHV partners; develop reliable, debuggable firmware, diagnostics, and deployment tools; contribute from pre-silicon design through production; make technical decisions amid ambiguity; and mentor architects and engineering teams.

Drive the development and roadmap of HPC and AI/ML products for physics, plasma, high-energy-density simulations, and resilient materials discovery. Define features, requirements, positioning, and go-to-market strategy; collaborate with engineering and NVIDIA software teams; identify customers and partnerships; and represent products at workshops and conferences.

Develop and enhance network hardware and software profiling tools using C/C++ and Python. Collaborate with customers, architects, hardware and software engineers, and operations teams to define requirements, design solutions, and deliver tools for optimizing complex GPU, CPU, and networking systems across HPC, cloud, AI, embedded, and automotive environments.

Lead the architecture, prototyping, deployment, and scaling of agentic AI platforms for NVIDIA Marketing. Translate marketing opportunities into reliable AI agents, reusable platform capabilities, and production architectures. Establish practices for evaluation, observability, security, governance, reliability, and cost optimization while partnering with engineers on integrations, tooling, retrieval, memory, orchestration, and infrastructure. Guide complex initiatives from discovery through ongoing operations and communicate technical solutions to technical and business stakeholders.

Lead and scale NVIDIA’s data labeling platform by managing software, data, and AI engineers; overseeing scalable annotation workflows, auto-labeling applications, data pipelines, analytics, interfaces, and operational tooling. Guide system architecture, hands-on Python development, multimodal model integration, platform reliability, and data integrity while aligning engineering priorities with executives, research leaders, and data operations teams.

Design and implement end-to-end GPU performance verification infrastructure. Build scalable data pipelines to collect metrics from simulation, emulation, and silicon. Collaborate with GPU architects and hardware teams to automate verification workflows, provide performance insights and modeling, and accelerate GPU build iteration.

Develop simulation infrastructure for evaluating deep learning workloads across NVIDIA GPUs and compiler stacks. Optimize compiler kernel code generation, computational graphs, and datacenter-scale AI deployments using performance modeling. Collaborate with architecture, software, product, and research teams to assess future GPU features and influence silicon and system design. The role requires strong MLIR, C/C++, and Python expertise, along with experience in compiler optimization, architectural simulation, or related areas.

Own the end-to-end hardware safety strategy for automotive sensor systems, including cameras, LiDAR, radar, and IMUs. Define safety requirements, evaluate supplier architectures and documentation, perform FMEA, FMEDA, DFA, and FTA analyses, and guide safety-critical design decisions. Collaborate with architecture, hardware, software, systems, validation, and supplier teams from concept through integration and verification. Produce safety cases, technical analyses, integration guidance, and user-facing documentation.

Lead the design, configuration, implementation, and support of SAP FICO solutions, with emphasis on Product Costing and Finance integrations. Translate business requirements into system solutions, coordinate global and cross-functional teams, guide projects, mentor users, manage testing and training, and support application roadmaps. The role also involves third-party finance application integration, change and release management, production support, and collaboration with finance stakeholders across time zones.

Develop AI-driven automated tests, tools, utilities, and frameworks for NVIDIA GPU device drivers and SDKs across Windows and Linux. Analyze test results, isolate defects, debug root causes, improve code coverage, validate software across hardware and interface matrices, and support CI/CD testing. The role includes code reviews, workflow optimization, task prioritization, reporting, and close collaboration with engineering leadership in a fully onsite environment.