Jobs at NVIDIA

The Senior DFT Engineer will design testing methodologies, work with cross-functional teams, mentor junior engineers, and enhance test access mechanisms and validation processes for complex semiconductor chips.

Support and improve NVIDIA AI Enterprise customer deployments. Develop tools and documentation, troubleshoot issues, and collaborate with engineering for resolutions.

Develop, validate, and support diagnostic test programs for NVIDIA GPUs/CPUs used in manufacturing. Interface with ASIC, ATE, firmware, validation, software, and product teams to resolve issues, drive process improvements, perform root cause analysis, and optimize test program performance and efficiency across manufacturing partners.

Support FP&A for Israeli R&D: forecasting, variance analysis, financial modeling for Networking hardware builds and bring-up. Partner with Engineering Business Management, NPI, and costing to review Prototype Build Requests, build dashboards, and implement AI/automation to improve decision-making and forecast accuracy.

Partner with universities and research institutes to design and deploy GPU-accelerated HPC and scientific AI solutions. Architect workflows from high-fidelity simulation to AI surrogates and foundation models, profile and optimize GPU training and inference, and collaborate with internal teams to align technical roadmaps. Support computational physics, multiphysics simulation, and engineering design exploration. Travel up to 20%.

The Lead Design Engineer will create and manage innovative designs for automotive harnesses and setups, collaborating with vendors and leading projects to meet engineering build expectations.

Lead product engineering for datacenter GPU systems through production ramp: define test specs, ensure DFx and signal integrity, optimize assembly and test flow, drive component qualifications and BOM updates, analyze volume data to resolve yield/test issues, and support customer escalations to meet cost, quality and throughput targets.

Partner with university researchers to enable GPU-accelerated training and inference for foundation models, multimodal AI, reasoning systems, and agents. Advise on distributed training, memory/throughput optimization, reproducible workflows, and evaluation; build prototypes, translate lab feedback, and support adoption across NVIDIA platforms. Travel up to 20%.

The role involves architecting next-gen GPU memory systems, optimizing performance, developing models, collaborating with teams, and mentoring engineers.

Analyze, profile, and optimize deep learning training workloads on NVIDIA GPUs; implement production-quality software across drivers to DL frameworks; build processor/system simulators and automation tools to enable architecture studies and workload optimization.

Lead mechanical manufacturing development from NPI to mass production for thermal and mechanical components. Ensure supplier readiness, tooling and line preparedness, DFM closure, risk analysis, yield and quality improvement, and cross-functional coordination with R&D, operations, suppliers, and quality.

Triage and resolve enterprise Ethernet/InfiniBand switch issues, develop features and tooling for NVIDIA networking products, collaborate with customers and engineering to reproduce and fix NOS/Linux-level problems, document solutions and improve product reliability and experience.

Support sales and ISV partners on NVIDIA Jetson edge computing solutions from pre-sales through production. Provide technical design reviews, board bring-up assistance, debugging, training, and deep hands-on engagement on complex computer-vision and robotics projects. Collaborate with global NVIDIA teams to guide Jetson product implementations and customer success.

Lead and execute PLM activities and SAP MM maintenance, administer PLM systems, coordinate cross-functional teams, define and document processes, and design automation, analytics, and AI-enabled solutions to improve PLM operations and scalability.

Design, build, and deliver scalable SAP IBP planning solutions (response & supply, demand). Support and enhance planning processes, prototypes, integrations, dashboards, and end-to-end material, supply, replenishment, deployment, and service-parts planning to improve profitability and operational performance.

Lead GTM technical engagements with data-platform ISVs to design architectures, build POCs, benchmarks, and enablement for NVIDIA acceleration across analytics, databases, and GenAI workflows. Create reference assets, demos, and training while partnering with product, engineering, sales, and customers.

Design, implement, and optimize GPU-accelerated deep learning kernels, kernel libraries, and inference runtime components. Build production-quality software (e.g., cuDNN, FlashInfer) and JIT/codegen systems, profile and tune performance for LLM inference, and collaborate across compilers, GPU architecture, and open-source ecosystems to ship scalable AI inference solutions.

Design and implement manufacturing test processes for GPU products, drive post-silicon characterization and ATE-based debug, analyze yield limiters, lead cross-functional NPI bring-up and validation, optimize test coverage and cost, and correlate production and customer failures to implement process improvements.

Design and build scalable automation, testing, and infrastructure for NVIDIA deep learning libraries across platforms. Develop full-stack tools from UIs to cluster and database layers, configure CI/CD, and advance industry-standard tooling to improve build, test, integration, and release workflows.

Lead architecture and roadmap for next-generation DRAM/HBM memory solutions, co-optimizing DRAM, GPU, packaging, and process technology. Drive HBM PHY/interface design, TSV/interposer integration, refresh/ECC schemes, and system-level trade studies. Collaborate with DRAM vendors and JEDEC, mentor engineers, and influence standards and vendor roadmaps to deliver high-performance, efficient, and reliable GPU memory solutions.