NVIDIA seeks an engineer to develop AI/ML-based tools and LLM integrations to automate performance verification workflows for GPUs, SoCs, and related systems. Responsibilities include building agents to detect regressions and root causes, designing data pipelines for model training, collaborating with verification teams, and measuring automation impact. Requires B.Tech/M.Tech/PhD in EE/CS or related field and 3+ years in hardware verification, performance validation, or EDA tool development.

Hands-on engineering role to design and optimize end-to-end training workflows for robotics foundation models (pre-training, fine-tuning, RL) across multi-GPU and multi-node environments. Requires 5+ years experience, an advanced degree or equivalent, and experience with frameworks and tools such as PyTorch, NVIDIA NeMo, JAX, Hugging Face, profiling tools, and robotics simulation/platforms (e.g., Isaac Sim, Cosmos, GR00T). Salary ranges provided for levels 3 and 4.

Lead strategy and execution for reinforcement-learning post-training frameworks and related systems (e.g., VeRL, Miles, TorchTitan, Ray, Megatron-Core) on NVIDIA platforms. Responsibilities include setting technical priorities, benchmarking and reproducibility, multi-team coordination across research, product, hardware and open-source partners, and hiring and developing managers and senior engineers. Requires MS/PhD or equivalent, 10+ years software engineering experience and 4+ years managing engineering teams; strong background in distributed AI systems, ML infrastructure, and high-performance computing.

Work across research, engineering, and product teams to develop workflows, profile and benchmark applications, build example/tutorial applications, and represent NVIDIA at scientific events. Requires 5+ years building HPC/AI/ML software for physics and numerical simulations and strong experience with GPU-accelerated computing and modern ML frameworks.

Role at NVIDIA working with inference partners to build, optimize, and operate large-scale GPU inference stacks (including Dynamo, Triton, TensorRT-LLM). Requires 6+ years in solutions architecture (2+ with AI on Kubernetes) and expertise in disaggregated serving, kernel optimization, caching, quantization, agent design, and DevOps for Kubernetes-based deployments.

Lead design and implementation of evaluation methodologies, benchmark environments, and evaluation infrastructure (pipelines, CI, statistical tooling) for reasoning, multimodal, long-context, and agentic systems. Partner with research, training, and product teams to translate evaluation signals into release decisions and training directions. Requires 6+ years of hands-on experience with LLMs/multimodal evaluations and strong statistical foundations.

NVIDIA seeks a Senior MLOps Engineer to collaborate with strategic customers to implement and optimize AI workloads, profile and tune large-scale training and inference, build MLOps pipelines, and support new hardware and open-source frameworks. Requires a BS/MS/PhD or equivalent, 8+ years technical experience, strong systems knowledge (Linux, Kubernetes, batch schedulers, distributed filesystems, datacenter networking), scripting and systems programming (Python, bash, C++/Go/Rust), and experience with deep learning frameworks. Listed salary ranges: $184,000–$287,500 (Level 4) and $224,000–$356,500 (Level 5).

NVIDIA is hiring engineers to work on CUDA Tile and TensorIR compiler technologies for GPUs. Responsibilities include designing and implementing compiler transformations, MLIR dialects and lowering passes, defining public APIs, and optimizing tile-based kernels for performance across NVIDIA GPU architectures. Required: BS/MS/PhD or equivalent and 3+ years' experience in compiler optimization, performance analysis, IR design, and strong C/C++ skills.

Design and implement lane-level navigation and localization systems using standard navigation maps for autonomous vehicles. Responsibilities include implementing efficient C++ code, evaluating algorithms on large real-world and synthetic datasets, productizing software for deployment to consumer cars, and following automotive safety and quality standards (MISRA, ISO26262). Requires BS/MS/PhD or equivalent, 8+ years experience, strong algorithm design skills, and excellent communication/teamwork.

NVIDIA is hiring an AI Compiler Engineer to work on kernel generation, computational graph optimizations, and compiler frameworks for next-generation GPUs. The role requires 3+ years of relevant industry compiler experience, hands-on MLIR knowledge, and strong C/C++ and Python programming and software design abilities. Collaboration with hardware, software, and research teams and experience with AI workloads (inference/training, LLMs, datacenter deployments) are emphasized.

Lead development of global strategy and architectures for scaled-out, high-throughput low-latency AI inference. Responsibilities include designing distributed pipelines and orchestration layers, hardware-software co-optimization (GPU/CUDA/kernels/drivers), model lifecycle automation (deployment, versioning, scaling), and collaborating with customers and open source ecosystems (TensorRT-LLM, vLLM, Ray, Kubernetes). Requires 16+ years technical experience and deep expertise in GPU acceleration and production distributed systems.

Role requires partnering with industry and account teams to define and deliver solutions that integrate NVIDIA technology into manufacturing and robotics use cases (datacenter and edge). Requires 8+ years in robotics/AI/humanoids, experience with GPU-based parallel computing, deep learning frameworks, containerization (Docker/Kubernetes), simulation tools (Isaac Sim/Lab), and customer-facing technical engagements. BS/MS/PhD in Robotics, Engineering, Computer Science or equivalent experience.

Design, build and optimize GPU-accelerated software and inference pipelines for state-of-the-art LLMs, multimodal and generative AI across NVIDIA accelerators. Contribute to and maintain inference frameworks (vLLM, SGLang, FlashInfer), use tools/plugins such as CUTLASS, Triton, NCCL and CUDA kernels, and work with cross-functional teams to drive performance improvements. Requires MSc/PhD or equivalent experience and 5+ years of relevant software development experience with strong C/C++ skills.

Responsible for crafting, scaling, and hardening deep learning infrastructure and libraries for multi-thousand GPU training clusters, improving data loaders, distributed training, scheduling, and performance monitoring, and owning orchestration and fault-resilient training systems. Requires 12+ years building high-performance distributed systems, strong Python and PyTorch experience, large-scale training expertise (DDP/FSDP, NCCL, parallelism), and systems knowledge including datacenter networking, Lustre, Slurm/Kubernetes, and storage.

Lead and scale an engineering team developing and optimizing open-source deep learning inference frameworks (vLLM/SGLang, FlashInfer, etc.) for NVIDIA GPUs. Responsibilities include roadmap and strategy, cross-team collaboration with compilers/libraries/research, performance tuning and profiling for LLMs and multimodal models, and mentoring engineers. Requires 6+ years software development experience and 3+ years technical leadership; strong C/C++ and GPU programming experience (CUDA, Triton, CUTLASS).

NVIDIA seeks a Senior Solutions Architect for generative AI with deep hands-on experience in large-scale GPU systems. Responsibilities include accelerating customer workloads, designing and optimizing AI clusters (compute, networking, storage, orchestration, observability), profiling distributed training/inference, diagnosing GPU/network/storage issues (InfiniBand, RoCE, RDMA, NCCL, NVLink, NVSwitch), and leading POCs and performance studies. Role requires ~6+ years experience. Occasional travel (~20%) and remote work are supported. Provided salary ranges: USD 184,000–287,500 (Level 4) and USD 224,000–356,500 (Level 5).

Role focuses on analyzing end-to-end performance of large-scale AI workloads, designing and executing performance studies and benchmarks, using profiling and observability to diagnose bottlenecks, and driving optimizations with platform and GPU architecture teams. Requires BS (or equivalent), 12+ years' experience, strong C++ and Python skills, and foundations in operating systems, computer architecture, distributed systems, and performance engineering. Experience with CUDA, GPU systems, and deep learning frameworks (PyTorch, JAX/XLA) is highlighted.

Role involves developing open-source software to serve GPU-hosted LLMs (vLLM, SGLang, TensorRT-LLM), extend Dynamo for agentic inference (long-horizon reasoning, tool calling, multi-turn state), optimize inference-state management across memory/storage (KV/prefix cache, NIXL), and evolve distributed inference frontends. Requires MS/PhD or equivalent, 10+ years experience, and strong Rust and Python skills.

NVIDIA seeks a Distinguished Engineer to lead the AI Safety & Security Engineering team, define harness architecture and engineering standards, guide the Find/Validate/Patch roadmap, and mentor founding engineers. Requires 18+ years building complex systems; experience in distributed systems, ML pipelines, productionizing research, and security or vulnerability research is highlighted. Expected base salary range: 320,000–488,750 USD per year.

Senior security research role (12+ years) focused on validation and patching methods for defined vulnerability classes. Responsibilities include confirming exploitable issues, producing and verifying safe fixes, defining quality and revalidation standards, documenting evidence for independent review, and using AI-assisted workflows while maintaining rigorous security judgment.