Lead technical subject-matter expert responsible for designing developer tools, APIs, and workflows for chemistry and materials science, translating ML and simulation research into production software, prioritizing features across teams, and representing NVIDIA at scientific venues. Requires a PhD or equivalent, 8+ years building AI/ML software for chemistry or materials applications, strong GPU and ML framework experience, and deep knowledge of interatomic potentials, generative modeling, and quantum chemistry workflows.

Role to optimize inference speed and carry out production deployment of Cosmos foundation models on diverse GPU platforms; profile and remove bottlenecks in deep learning workloads. Requires 5+ years' experience, MSc/PhD or equivalent, strong deep learning background, Python and PyTorch skills, and experience with inference optimization techniques and frameworks (e.g., TensorRT, vLLM).

Join NVIDIA Research Singapore to research and design efficient generative AI and foundation models (LLMs, visual generation, robotics) optimized for NVIDIA GPUs. Responsibilities include publishing original research, collaborating with internal and external teams, mentoring interns, and transferring technology to product groups. Requires a PhD in CS or EE, ~4 years' experience, strong Python prototyping skills; C++ and CUDA and familiarity with PyTorch are a plus.

Work on designing and optimizing kernels, extensible abstractions for LLM serving engines, JIT domain-specific compilers, and inference runtimes for NVIDIA hardware. Qualifications include a Master's (PhD preferred) or equivalent experience, 6+ years in ML/DL systems development, experience with frameworks such as PyTorch/JAX/TensorFlow/ONNX, and expertise in GPU kernel development and compilers. Base salary range listed as 184,000 USD - 287,500 USD per year.

Join NVIDIA to develop AI inference systems, including kernel implementations, code generators, and runtimes to accelerate LLMs and other AI workloads. Requires a Master's (or equivalent) preferred PhD, 6+ years of ML/DL systems development experience, and strong experience with Python, C/C++, CUDA, deep learning frameworks, and GPU kernel performance optimization. Base salary range: 184,000 USD - 287,500 USD.

Role focuses on analyzing large-scale telemetry and workload data on GPU-accelerated clusters, building practical visualizations and lightweight ML/DL implementations, and communicating findings to engineers and leadership. Requires 5+ years analyzing complex datasets, strong Python and JavaScript skills, hands-on experience with telemetry/observability stacks (e.g., Grafana, Elasticsearch, Splunk), and familiarity with core ML concepts. Base salary range listed: 152,000 USD - 241,500 USD per year.

Role focused on architecting and implementing next-generation GPU profiling features, building functional and performance models, and creating test plans. Requires a Masters/PhD or equivalent, 3+ years in computer architecture/ASIC design/verification or software development, strong C++ and Python, experience with performance modeling/hardware simulation (ideally SystemC), and familiarity with GPU profiling and AI/HPC workloads. Base salary ranges provided for Levels 4 and 5 (overall range in posting: USD 184,000–356,500).

Work on analyzing deep learning networks and implementing compiler optimization algorithms for AI inference across NVIDIA GPUs. Collaborate with deep learning framework and GPU architecture teams; require 3+ years relevant experience, strong C/C++ and Python skills, and experience with compiler technologies (e.g., MLIR, LLVM, XLA, Triton).

NVIDIA is hiring a Senior Machine Learning Applications and Compiler Engineer to build and maintain high-performance runtime and compiler components for the LPX inference stack. Responsibilities include mapping large-scale inference workloads to NVIDIA systems, extending software ecosystem libraries and tooling, benchmarking and profiling for performance, and collaborating with hardware architects. Requires MS/PhD (or equivalent) with 6 years relevant experience, strong systems programming skills (C/C++ and/or Rust), compiler/runtime development experience (IR design, passes, code generation), familiarity with LLVM/MLIR, and experience with TensorFlow, PyTorch, and ONNX.

Join NVIDIA's Architecture Modeling group to model GPU architectures and baseboard components, develop and validate functional and performance simulation models, and build testing infrastructure. Requires 5+ years' experience, strong C/C++ and scripting skills (Python/Perl), familiarity with SystemC/TLM, and experience with ML frameworks such as PyTorch and TensorFlow.

Role responsible for improving ML training and inference efficiency across GPU clusters by debugging and optimizing software and infrastructure, building tools and frameworks to detect bottlenecks, collaborating with researchers across domains (LLMs, robotics, autonomous vehicles, video), and working with technologies such as CUDA, NCCL, Nsight, distributed training, cloud platforms, and fast distributed storage.

Work on implementing and optimizing language and multimodal model inference in NVIDIA Inference Microservices and TRT-LLM. Requires 5+ years experience, strong deep learning and inference background, performance profiling/optimization (especially GPU), and proficiency in C++ and PyTorch.

Design and evaluate GPU and system architectures to improve AI inference performance and efficiency. Build power and performance models of the AI inference stack, implement and test deep learning algorithms, and collaborate across software, research, and product teams. Requires MS or PhD (or equivalent), 5+ years relevant experience, strong math foundation in ML/DL, expert programming in C/C++/Python, and familiarity with GPU computing (CUDA) and HPC (MPI/OpenMP).

Role involves building high-performance compiler and runtime components to map large-scale neural network inference workloads to NVIDIA platforms. Requires hands-on experience with compiler/runtime development (LLVM/MLIR), systems-level programming (C++/Rust), deep learning frameworks (TensorFlow/PyTorch/ONNX), and performance profiling/optimization. Minimum 5 years relevant experience and MS/PhD or equivalent experience.

Work on high-performance runtime and compiler components for NVIDIA's LPX inference stack: map large-scale inference workloads to hardware, extend integrations with the software ecosystem, and benchmark/profile to drive performance. Requires MS/PhD or equivalent with 5+ years' relevant experience, strong systems programming (C++ and/or Rust), compiler/runtime experience (IR, optimization passes, codegen), familiarity with LLVM/MLIR, and experience with TensorFlow/PyTorch and ONNX.

Design, build, and maintain AI infrastructure software and tools for large-scale pre-training, post-training, and inference. Responsibilities include improving efficiency and resiliency, co-designing APIs with resiliency stacks, defining reliability metrics, and performing root-cause analysis from application to hardware. Requires 8+ years experience, proficiency in Python and C/C++, experience with observability platforms (ELK, Prometheus, Loki), distributed systems, AI training/inference infrastructure and strong debugging skills.

New college grad (2026) position building ML and AI systems for circuit design and design automation (VLSI/ASIC/EDA). Work includes silicon data analysis, SPICE correlation, circuit optimization, design-space exploration, and integrating models with ML and visualization tools. Requires a Master's or PhD (or equivalent experience) and proficiency in Python and C++.

Work on integrating and optimizing communication libraries (NCCL, NVSHMEM, GPUDirect) into AI frameworks, perform workload and performance analysis on multi-GPU clusters, author fused compute/communication kernels, improve compiler/automation for communications, and influence communication library roadmaps. Requires 5+ years of software engineering and HPC/AI experience and strong skills in Python, C++, CUDA and deep learning frameworks.

Role focuses on developing and scaling automated inference and deployment solutions (TRT Model Optimizer) for generative AI models such as LLMs and diffusion models. Responsibilities include extracting model graph representations from PyTorch models, building optimization techniques (quantization, pruning, sharding, efficient kernels), profiling GPU performance, and architecting a modular deployment platform. Requires MS/PhD or equivalent experience and 5+ years in deep learning; strong Python, PyTorch, CUDA, and performance-analysis skills. Salary ranges provided (USD yearly).

NVIDIA seeks a Manager for GPU Accelerated Data Analytics to drive research and development of performance optimizations across CPU and GPU platforms. Responsibilities include technical leadership, driving innovation in algorithms and low-level optimizations (CUDA, C/C++), building and mentoring a distributed team of performance engineers, and collaborating with cross-functional partners. Requires MS/PhD or equivalent, 7+ years relevant experience with 3+ years in engineering leadership, and strong expertise in CPU/GPU architecture and performance engineering.