NVIDIA seeks a Senior Architect to guide software design, architecture, and implementation of cost, power, and performance modeling for datacenter-scale GPU-accelerated platforms. Requires 8+ years in systems architecture and modeling, strong Python skills, and experience with datacenter/server architecture, parallel computing, data analysis, and DevOps practices.

Lead software architecture for NVIDIA deep learning server platforms, driving design through production and partnering with application, system software, firmware teams, customers and vendors. Requires 10+ years in system architecture, experience with SW/HW interface, HPC/deep learning workloads, out-of-band/in-band management and device management protocols (e.g., Redfish, IPMI, MCTP, PLDM, RDE). Locations include US (Austin, Hillsboro, Santa Clara) and remote; provided salary ranges from 224,000 USD to 431,250 USD per year.

NVIDIA seeks a Senior Software and System Architect to lead architecture for DPUs and NICs, cloud-networking and security solutions. Requires 5+ years as a software or systems architect, strong software development experience (C, Python), Linux development, and knowledge of networking, DPDK/OVS/OVN, containers and Kubernetes.

Lead and manage a team developing accelerated image and data processing and compression solutions, define project goals and roadmaps, ensure execution and delivery, and collaborate with internal/external customers and the open source community. Requires a Bachelor’s/Master’s (or equivalent) and at least 5 years of experience including 3+ years managing teams; strong C++ and Python skills; experience with parallel programming and performance optimization on modern architectures (CUDA, MPI, NVSHMEM, NCCL, AVX). Location listed as US, CA, Remote. Expected base salary range: 200,000–322,000 USD per year.

Work on designing, implementing, and optimizing scalable numerical dense linear algebra software for GPUs, provide technical leadership to engineers and QA, and collaborate with product management to meet feature and performance goals. Requires MSc/PhD or equivalent and 5+ years developing and optimizing high-performance numerical linear algebra software using C++ and parallel programming (CUDA, MPI, OpenMP, OpenACC, pthreads).

NVIDIA seeks a Senior Math Libraries Engineer to scope, design, and implement numerical dense linear algebra software for AI and HPC on GPUs. Role requires C++ experience (8+ years), expertise in kernel generation, JIT/composable library design, performance optimization, collaboration across teams, and technical leadership. Preferred experience includes CUDA, parallel programming models (MPI/OpenMP/OpenACC/pthreads), assembly for performance, Python scripting, and familiarity with ML/DL and GPU hardware.

Role involves designing, implementing, and maintaining automated tests (primarily Python), owning end-to-end feature verification, and validating software via system and integration testing in Linux-based and virtualized environments. Requires 5+ years experience in software testing/QA automation, strong Linux and networking knowledge, and fluency in English.

Senior HPC architect role responsible for designing, deploying, and bringing up large-scale GPU compute clusters and performance platforms. Requires 8+ years in accelerated/datacenter/HPC computing, programming/scripting in C/C++/Python/Bash, experience with parallel filesystems and scheduling/I/O stacks; familiarity with containers, telemetry/visualization, and Linux performance tools is desirable.

Work on communication libraries (e.g., NCCL, NVSHMEM, UCX) and co-design hardware/software solutions to accelerate AI and HPC workloads. Responsibilities include identifying communication bottlenecks, designing and implementing new communication technologies, building proofs-of-concept and simulations at large GPU scales. Requires MS/PhD or equivalent, 5+ years experience, strong C/C++, parallel programming and Linux skills, and knowledge of networking and system architecture.

Senior Math Libraries Engineer role to design, implement, and optimize C++/CUDA systems for unstructured sparsity in deep learning and HPC on NVIDIA GPUs; enable integration with Python/PyTorch, evaluate performance on real applications, and collaborate with product and partners. Requires 6+ years of high-performance software development experience.

Work on cloud-native data and storage services (dataset discovery, ingestion, governance, checkpointing, observability, low-latency access) to support exabyte-scale, high-performance GPU training and inference. Requires BS (or equivalent) and 5+ years software engineering experience with strong foundations in algorithms, data structures, distributed systems, and experience with Kubernetes, cloud providers (AWS/GCP/Azure) and languages such as Go, Python, Rust, C/C++, or Java. Collaboration with product, platform, SRE, and operations teams and focus on reliability, performance, and production readiness.

NVIDIA is hiring a Senior Deep Learning Software Engineer for inference to design, build, and optimize GPU-accelerated software for large-scale model serving. The role involves performance tuning of LLM, multimodal and generative AI models across NVIDIA accelerators, contributing to inference libraries (vLLM, SGLang, FlashInfer), and using tools like CUTLASS, Triton, NCCL and CUDA. Requires a Master's/PhD or equivalent experience and 6+ years of relevant software development experience with strong C/C++ skills; Python and production inference/deployment experience are advantageous.

Lead technology strategy and system architecture for next-generation wireless infrastructure including AI-for-RAN, agentic packet core, ISAC, and semantic communications. Drive prototyping, performance optimization, industry standards engagement (3GPP, O-RAN), and integration of acceleration technologies. Requires MS/PhD or equivalent, 20+ years industry experience, deep expertise in communications theory, RAN (PHY/L1, L2), UPF, AI/ML, and hands-on programming in Python, C/C++, and MATLAB.

Work on GPU-accelerated sparse linear algebra libraries (e.g., cuSPARSE, cuDSS) developing and optimizing kernels for sparse BLAS operations, direct and iterative solvers, preconditioners, and AMG. Requires strong C++ and parallel programming experience (CUDA, MPI, OpenMP, OpenACC, pthreads), expertise in floating point arithmetic and sparse linear algebra, and 5+ years of relevant experience.

NVIDIA seeks a Senior Software Engineer to design and implement JAX core components, drive performance on NVIDIA products, and collaborate with researchers and cross-functional teams. Requires 6+ years' experience, strong C/C++ and Python skills, knowledge of ML framework internals, CPU/GPU architectures, numeric libraries, and experience with distributed training and performance optimization.

Join the storage management plane team to maintain and develop Kubernetes operators and the Container Storage Interface (CSI) plugin, and to build a web-based solution to manage and monitor distributed storage. Required: B.Sc./M.Sc./Ph.D. or equivalent experience, 8+ years web development (client and server), proven Kubernetes experience (operators/CSI), Node.js (server-side), scripting with Python/Bash, and 5+ years working in Linux. Preferred technologies listed include React, jQuery, c3j, HTML5/CSS3/Bootstrap, Websockets, Kafka, MongoDB, Express, async, Git. Salary ranges provided: 184,000–287,500 USD (Level 4) and 224,000–356,500 USD (Level 5).

Role owns end-to-end modules and projects focused on high-performance communication and memory management libraries for distributed AI. Responsibilities include hardware-software co-optimization (GPUDirect RDMA, NVLink), profiling and optimizing data movement across GPU memory, DRAM, NVMe and networks, integrating networking into AI serving stacks, contributing to open-source, and mentoring. Requires 12+ years systems software/networking experience and advanced degree or equivalent.

Lead design and implementation of a unified, multi-tier memory/storage layer spanning GPU memory, host memory, RDMA-accessible memory, SSDs, and remote storage to support large-scale LLM inference. Integrate with LLM serving engines (vLLM, SGLang, TensorRT-LLM) for KV-cache offload and sharing, collaborate with GPU architecture and networking teams (GPUDirect, RDMA, NVLink), mentor engineers, and drive technical direction. Requires 15+ years experience in distributed systems, high-performance storage, or ML infrastructure and strong skills in C/C++, Python, memory hierarchies, and networked I/O.

Role involves understanding, profiling, and optimizing DL training and inference on state-of-the-art hardware; collaborating across research and engineering teams; implementing production-quality software and building automation tools. Requires 5+ years experience, strong DL and computer-architecture background, and programming skills in C++ and Python; GPU programming (CUDA/OpenCL) and LLM framework experience are advantageous.