NVIDIA seeks an Applied AI Engineer to architect and deploy AI solutions (LLM-powered pipelines, agents, orchestration) for silicon co-design and post-silicon validation. Requires 5+ years building/deploying ML/AI systems or data-intensive backend services, 2+ years owning applied AI/LLM workflows, strong Python and at least one static language (C/C++/C#/Java/Scala), and experience with production test, validation, or silicon debug.

NVIDIA is hiring a Senior Deep Learning Inference Software Engineer to design, build, and optimize GPU-accelerated software and inference frameworks (vLLM, SGLang, FlashInfer). The role centers on performance tuning for LLMs, multimodal and generative AI across NVIDIA accelerators and requires strong C/C++ skills, 5+ years' software development experience, and GPU/CUDA experience. Salary ranges for Poland are provided.

NVIDIA seeks a Senior Solutions Architect for Industrial Engineering to advise CAE/CFD/FEA ISVs, simulation platforms, and OEMs. Role involves accelerating solvers on NVIDIA GPUs/HPC, applying physics-ML and surrogate models, using Omniverse for digital twins, analyzing application architectures, and delivering trainings and demos. Requires 8+ years in computational engineering or simulation, strong numerical methods background, and hands-on experience with tools like Ansys, OpenFOAM, and languages such as Python and C/C++.

Hands-on Solutions Architect role focused on designing, deploying, and operating Kubernetes-based platforms for distributed robotics and Physical AI workloads (simulation, synthetic data, training, inference). Requires experience with GPU acceleration, NVIDIA inference tooling, workflow orchestration (Airflow/Argo), DevOps practices (GitOps, IaC, observability), networking and storage systems, and customer-facing technical leadership.

Lead technical engagements with customers and partners to define and deliver solutions using NVIDIA platforms for AI, deep learning, HPC, and edge deployment. Requires BS/MS/PhD or equivalent and 8+ years' experience in robotics, humanoids, and AI domains, experience with GPU-based parallel computing, containerization (Docker, Kubernetes), deep learning frameworks (e.g., TensorFlow, PyTorch, TensorRT), and strong communication and customer-facing skills.

NVIDIA is hiring a Senior Deep Learning Software Infrastructure Engineer to craft, scale, and harden training libraries and infrastructure for large-scale autonomous driving model training. The role requires designing orchestration and distributed training systems, optimizing data loaders and performance, and collaborating with researchers and platform teams. Candidates should have 12+ years building high-performance distributed systems and strong experience with PyTorch, large-scale training (DDP/FSDP), NCCL, datacenter networking, parallel filesystems, schedulers (Slurm/Kubernetes), and Python.

Lead and scale an engineering team building and optimizing open-source GPU-accelerated deep learning inference frameworks (vLLM, SGLang, FlashInfer). Responsibilities include driving roadmap and execution for Client AI inference frameworks, partnering with compiler/libraries/research teams, performance tuning and profiling of LLMs and multimodal models, and guiding best practices for CUDA, Triton, CUTLASS and multi-GPU communications (NCCL, NVSHMEM). Requires MS/PhD or equivalent, 6+ years software development experience (3+ years technical leadership), strong C/C++ skills, hands-on GPU programming and proven experience deploying/optimizing deep learning models in production. Salary range provided: 184,000–356,500 USD/year.

Role at NVIDIA to lead architecture and performance work on Dynamo, building integrations with vLLM, SGLang, and TensorRT-LLM; responsibilities include finding and removing bottlenecks across runtimes, kernels, networking, and orchestration, and developing inference optimizations such as scheduling, disaggregation, KV caching, and autoscaling. Requires 3+ years experience and strong skills in Python/Rust/C++ and ML inference techniques.

NVIDIA is seeking an engineer to research, prototype, and implement systems optimizations at the intersection of deep learning frameworks and low-level CUDA. Responsibilities include designing and optimizing custom CUDA kernels, architecting distributed cluster-scale systems, analyzing hardware-software interactions, developing profiling and runtime tools, and collaborating with researchers and compiler/driver teams. Requires a BS/MS/PhD or equivalent, 2+ years relevant experience, strong C++ and Python skills, familiarity with CUDA and GPU architectures, and experience with transformers and distributed ML.

Work on research-oriented projects at the intersection of CUDA and deep learning systems, including custom high-performance CUDA kernel development, profiling and optimizing generative AI models and transformers, and architecting distributed systems that scale from single GPUs to supercomputer clusters. Requires strong C++ and Python skills, deep learning background, GPU/CUDA experience, and 8+ years of relevant experience.

Join NVIDIA's Infrastructure Specialists team to set up, tune, and validate AI factory environments across multi-GPU/multi-node Linux clusters. Responsibilities include running and analyzing AI/LLM benchmarks, diagnosing failures/hangs/low performance, applying observability and automation (Python/Shell) for benchmarking and regression checks, and collaborating with hardware/software/network/product teams. Requires 5+ years managing Linux systems in HPC/distributed/AI environments and hands-on experience with multi-GPU or multi-node AI/ML workloads and NCCL/collective communication patterns.

NVIDIA seeks a Senior Solutions Architect with 6+ years in AI infrastructure, HPC, networking, or SRE to design, profile, and optimize large-scale GPU clusters (on-prem and cloud). Responsibilities include troubleshooting GPU networks (InfiniBand, RoCE, RDMA), debugging NCCL, leading POCs, and partnering with engineering and sales. Remote work is supported; occasional travel (~20%) required.

Manage and scale an engineering team developing and optimizing open-source deep learning inference frameworks (examples: SGLang, vLLM, FlashInfer) for NVIDIA GPUs. Responsibilities include roadmap and execution, cross-team partnership with compiler and libraries teams, performance tuning and profiling of LLMs and multimodal models, and fostering technical excellence. Requires MS/PhD or equivalent, 6+ years software development experience (including 3+ years in technical leadership), strong C/C++ skills, GPU programming experience (CUDA, Triton, CUTLASS), and production experience deploying or optimizing deep learning models.

Lead design and implementation of AI solutions (LLM-powered pipelines, agentic workflows) to accelerate post-silicon validation and automation across NVIDIA's silicon toolchain. Requires 5+ years building/deploying ML/AI systems, 2+ years owning applied AI/LLM workflows, strong Python and at least one static language, EE fundamentals, and hands-on silicon lab/debug experience. Salary ranges provided for Levels 3 and 4.

NVIDIA is hiring an Applied AI Engineer to architect and deploy LLM-powered validation pipelines and AI integrations across silicon design and verification workflows. The role requires 5+ years building and deploying ML/AI systems or data-intensive backend services, strong Python and at least one static language (C/C++/C#/Java/Scala), experience with silicon characterization and lab debug tools, and familiarity with modern deep learning frameworks and agent/orchestration tools. Locations listed are across the US (including Santa Clara, CA) and remote; posted salary ranges from 152,000 USD to 287,500 USD per year depending on level and location.

Role owns the end-to-end evaluation flywheel: strategy and architecture for turning driving and simulation data into curated golden datasets, measuring metrics (precision/recall), ensuring evaluation quality and release processes, building self-serve dataset and metric tooling, and partnering with engineers and AI model developers. Requires 12+ years building software with significant experience in autonomous vehicles, robotics, or large-scale ML systems.

Work on Dynamo's open-source GPU-accelerated inference stack, extending disaggregated serving across vLLM, SGLang, and TensorRT-LLM to support agentic inference (long-horizon reasoning, tool calling, stateful multi-turn execution). Responsibilities include inference-state management (KV/prefix caches, memory/storage hierarchies), building distributed frontends, optimizing throughput and latency, and contributing to upstream API compatibility. Requires 10+ years' experience, strong Rust and Python skills, and expertise in LLM inference and GPU optimization.

NVIDIA seeks a Distinguished Engineer to found and lead the AI Safety & Security Engineering team, owning core harness architecture, baseline engineering standards, and the Find/Validate/Patch roadmap. The role requires mentoring founding engineers, bridging research to production, and strong experience (Bachelor's or equivalent and 18+ years) in architecting distributed systems, ML pipelines, or large-scale platforms. Base salary range: 320,000 USD - 488,750 USD.

Role focused on designing, implementing, and maintaining an agent harness and the systems to run and reproduce experiments for AI safety and security. Requires 5+ years software engineering experience, strong Python skills, familiarity with containerized environments and CI/CD, and experience with agent frameworks/ML infrastructure (NeMo experience is a plus). Base salary range: 152,000 USD - 241,500 USD.

NVIDIA is hiring a Machine Learning Engineer to work on product security, content safety, ML fairness, and robustness for LLMs and multimodal models. Responsibilities include developing datasets and models for safety evaluation, implementing bias-detection and mitigation techniques, defining responsible-use metrics, contributing to MLOps and safety tools, and collaborating with engineers and researchers. Requires a Master's/PhD or equivalent experience, 2+ years deploying ML models to production, strong ML fundamentals, Python and framework experience (PyTorch/Keras), and background in content safety, fairness, robustness, or model security. Posted salary ranges (annual) span 124,000–195,500 USD for Level 2 and 152,000–241,500 USD for Level 3.