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NVIDIA seeks a Senior Network Engineer to develop cloud network infrastructure. Responsibilities include architecting, implementing, and automating scalable network solutions for software development workflows.

Lead cross-stack adoption of modern toolchains, build and code-health practices, and performance workflows on NVIDIA CPU platforms. Integrate and validate compilers, linkers, cross-compilation, and CI; profile and optimize large codebases; build automation and playbooks; drive integration blockers to owners and communicate measurable before-and-after results to engineering teams and leadership.

Technical leader who bridges product engineering and operations for networking semiconductors. Ensures manufacturing readiness, monitors yield and risks, coordinates cross-functional issue resolution, supports NPI-to-mass-production transitions, and communicates product health to engineering, operations, supply chain, and executives.

Optimize and implement deep learning model kernels and libraries (e.g., Transformer Engine) for efficient scaling on NVIDIA GPUs. Collaborate on systems research, benchmark and submit results to community standards (MLPerf), engage with open-source and enterprise partners, and influence hardware and platform design.

Develop and implement compiler optimization algorithms and code generation for deep learning workloads (XLA/OpenXLA) targeting NVIDIA GPUs. Design graph partitioning and tensor sharding for distributed training/inference, performance tune models, work with ML frameworks (JAX) and hardware teams, and contribute to compiler/tooling (MLIR/LLVM/Triton).

Design, develop, and operate large-scale SDN control and data plane software (OVS/OVN/OpenFlow) for NVIDIA AI Cloud. Build network orchestration and IaC (gRPC/REST, Ansible, Terraform, ArgoCD/Flux), maintain CI/CD pipelines, implement observability/monitoring, drive upstream open-source contributions, and collaborate with SRE/DevOps to ensure production reliability and performance SLAs.

Design and implement GPU cluster scheduling and orchestration features, optimize resource usage and performance for large multi-node AI workloads, develop automation and batch workload management, perform performance analysis and root-cause investigations, support users on scheduler issues, and build the GPU-accelerated computing ecosystem.

The role involves designing and optimizing GPU architectures for AI Inference, analyzing hardware-software relationships, and developing performance models.

The Senior Hardware Systems Engineer will lead hardware pathfinding for LPU platforms, manage system architecture decisions, and oversee system debugging and program execution while collaborating with multiple teams to deliver production-ready systems.

The Senior Software Architect will design and implement communication technologies, improve performance for AI and HPC workloads, and lead innovative GPU communication solutions.