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NVIDIA is at the forefront of the generative AI revolution, building the software and systems that power the world’s most advanced large language model workloads. We are looking for a Software Engineer focused on bring-up, triage, benchmarking, analysis, and optimization of distributed training and inference workloads across NVIDIA GPU platforms at the largest scales we run. In this role you will help bring up, benchmark, and debug distributed LLM workloads on multi-GPU and multi-node deployments, and own the design and implementation of the benchmarking tooling, automation, and debugging workflows that support them. This is a hands-on role for an engineer who enjoys deep technical problems across deep learning systems, GPU performance, distributed computing, and large-scale operations. What you’ll be doing: Bring up, validate, and debug large-scale AI clusters, infrastructure, and end-to-end workloads. Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and adjacent NVIDIA AI software stacks. Perform root-cause analysis of failures in large distributed environments Contribute to the resilience and failure-attribution tooling that detects, triages, and attributes node, fabric, and workload failures across the cluster. Build and maintain repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms. Tune runtime settings, communication parameters, and deployment configurations in close partnership with framework, systems, and platform teams. Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization. What we need to see: Bachelor’s or Master’s in Computer Science or a related technical field (or equivalent experience). Experience developing software for AI, HPC, or systems-level applications. Hands-on experience with multi-GPU or multi-node workloads and CUDA-aware distributed execution. Background with debugging and scaling distributed systems. Experience debugging and triaging AI applications across the full stack, from the application level toward the hardware. Experience operating workloads in scheduled, containerized cluster environments. Excellent analytical, debugging, and communication skills, and a collaborative approach across teams. Strong Python and C/C++ programming skills. Ways to stand out from the crowd: Hands-on experience with NCCL and CUDA-aware distributed execution. Deep familiarity with the RDMA software stack (NCCL, IB verbs, UCX, libfabric) and with InfiniBand / RoCE congestion debugging. Experience building acceptance tests, benchmark harnesses, regression gates, or cluster qualification tooling for AI platforms, including MLPerf. Experience diagnosing performance jitter Experience building resilience, fault-detection, or failure-attribution systems for datacenter-scale infrastructure. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you’re creative, autonomous, and love a challenge, we want to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 108,000 USD - 178,250 USD for Level 1, and 124,000 USD - 195,500 USD for Level 2. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 3, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

NVIDIA is at the forefront of the generative AI revolution, building the software and systems that power the world’s most advanced large language model workloads. We are looking for a Software Engineer focused on bring-up, triage, benchmarking, analysis, and optimization of distributed training and inference workloads across NVIDIA GPU platforms at the largest scales we run. In this role you will help bring up, benchmark, and debug distributed LLM workloads on multi-GPU and multi-node deployments, and own the design and implementation of the benchmarking tooling, automation, and debugging workflows that support them. This is a hands-on role for an engineer who enjoys deep technical problems across deep learning systems, GPU performance, distributed computing, and large-scale operations. What you’ll be doing: Bring up, validate, and debug large-scale AI clusters, infrastructure, and end-to-end workloads. Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and adjacent NVIDIA AI software stacks. Perform root-cause analysis of failures in large distributed environments Contribute to the resilience and failure-attribution tooling that detects, triages, and attributes node, fabric, and workload failures across the cluster. Build and maintain repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms. Tune runtime settings, communication parameters, and deployment configurations in close partnership with framework, systems, and platform teams. Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization. What we need to see: Bachelor’s or Master’s in Computer Science or a related technical field (or equivalent experience). Experience developing software for AI, HPC, or systems-level applications. Hands-on experience with multi-GPU or multi-node workloads and CUDA-aware distributed execution. Background with debugging and scaling distributed systems. Experience debugging and triaging AI applications across the full stack, from the application level toward the hardware. Experience operating workloads in scheduled, containerized cluster environments. Excellent analytical, debugging, and communication skills, and a collaborative approach across teams. Strong Python and C/C++ programming skills. Ways to stand out from the crowd: Hands-on experience with NCCL and CUDA-aware distributed execution. Deep familiarity with the RDMA software stack (NCCL, IB verbs, UCX, libfabric) and with InfiniBand / RoCE congestion debugging. Experience building acceptance tests, benchmark harnesses, regression gates, or cluster qualification tooling for AI platforms, including MLPerf. Experience diagnosing performance jitter Experience building resilience, fault-detection, or failure-attribution systems for datacenter-scale infrastructure. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you’re creative, autonomous, and love a challenge, we want to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 108,000 USD - 178,250 USD for Level 1, and 124,000 USD - 195,500 USD for Level 2. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 3, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

NVIDIA is at the forefront of the generative AI revolution, building the software and systems that power the world’s most advanced large language model workloads. We are looking for a Software Engineer focused on bring-up, triage, benchmarking, analysis, and optimization of distributed training and inference workloads across NVIDIA GPU platforms at the largest scales we run. In this role you will help bring up, benchmark, and debug distributed LLM workloads on multi-GPU and multi-node deployments, and own the design and implementation of the benchmarking tooling, automation, and debugging workflows that support them. This is a hands-on role for an engineer who enjoys deep technical problems across deep learning systems, GPU performance, distributed computing, and large-scale operations. What you’ll be doing: Bring up, validate, and debug large-scale AI clusters, infrastructure, and end-to-end workloads. Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and adjacent NVIDIA AI software stacks. Perform root-cause analysis of failures in large distributed environments Contribute to the resilience and failure-attribution tooling that detects, triages, and attributes node, fabric, and workload failures across the cluster. Build and maintain repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms. Tune runtime settings, communication parameters, and deployment configurations in close partnership with framework, systems, and platform teams. Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization. What we need to see: Bachelor’s or Master’s in Computer Science or a related technical field (or equivalent experience). Experience developing software for AI, HPC, or systems-level applications. Hands-on experience with multi-GPU or multi-node workloads and CUDA-aware distributed execution. Background with debugging and scaling distributed systems. Experience debugging and triaging AI applications across the full stack, from the application level toward the hardware. Experience operating workloads in scheduled, containerized cluster environments. Excellent analytical, debugging, and communication skills, and a collaborative approach across teams. Strong Python and C/C++ programming skills. Ways to stand out from the crowd: Hands-on experience with NCCL and CUDA-aware distributed execution. Deep familiarity with the RDMA software stack (NCCL, IB verbs, UCX, libfabric) and with InfiniBand / RoCE congestion debugging. Experience building acceptance tests, benchmark harnesses, regression gates, or cluster qualification tooling for AI platforms, including MLPerf. Experience diagnosing performance jitter Experience building resilience, fault-detection, or failure-attribution systems for datacenter-scale infrastructure. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you’re creative, autonomous, and love a challenge, we want to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 108,000 USD - 178,250 USD for Level 1, and 124,000 USD - 195,500 USD for Level 2. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 3, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

NVIDIA is at the forefront of the generative AI revolution, building the software and systems that power the world’s most advanced large language model workloads. We are looking for a Software Engineer focused on bring-up, triage, benchmarking, analysis, and optimization of distributed training and inference workloads across NVIDIA GPU platforms at the largest scales we run. In this role you will help bring up, benchmark, and debug distributed LLM workloads on multi-GPU and multi-node deployments, and own the design and implementation of the benchmarking tooling, automation, and debugging workflows that support them. This is a hands-on role for an engineer who enjoys deep technical problems across deep learning systems, GPU performance, distributed computing, and large-scale operations. What you’ll be doing: Bring up, validate, and debug large-scale AI clusters, infrastructure, and end-to-end workloads. Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and adjacent NVIDIA AI software stacks. Perform root-cause analysis of failures in large distributed environments Contribute to the resilience and failure-attribution tooling that detects, triages, and attributes node, fabric, and workload failures across the cluster. Build and maintain repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms. Tune runtime settings, communication parameters, and deployment configurations in close partnership with framework, systems, and platform teams. Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization. What we need to see: Bachelor’s or Master’s in Computer Science or a related technical field (or equivalent experience). Experience developing software for AI, HPC, or systems-level applications. Hands-on experience with multi-GPU or multi-node workloads and CUDA-aware distributed execution. Background with debugging and scaling distributed systems. Experience debugging and triaging AI applications across the full stack, from the application level toward the hardware. Experience operating workloads in scheduled, containerized cluster environments. Excellent analytical, debugging, and communication skills, and a collaborative approach across teams. Strong Python and C/C++ programming skills. Ways to stand out from the crowd: Hands-on experience with NCCL and CUDA-aware distributed execution. Deep familiarity with the RDMA software stack (NCCL, IB verbs, UCX, libfabric) and with InfiniBand / RoCE congestion debugging. Experience building acceptance tests, benchmark harnesses, regression gates, or cluster qualification tooling for AI platforms, including MLPerf. Experience diagnosing performance jitter Experience building resilience, fault-detection, or failure-attribution systems for datacenter-scale infrastructure. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you’re creative, autonomous, and love a challenge, we want to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 108,000 USD - 178,250 USD for Level 1, and 124,000 USD - 195,500 USD for Level 2. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 3, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

NVIDIA is hiring experienced software engineers with kubernetes experience to help scale up its AI Infrastructure. We expect you to have significant software engineering experience with kubernetes including cluster operations, operator development, node health monitoring and working with GPU resource scheduling. We welcome out-of-the-box thinkers who can provide new ideas with strong execution bias. Expect to be constantly challenged, improving, and evolving for the better. You will help advance NVIDIA's capacity to build and deploy leading infrastructure solutions for a broad range of AI-based applications. If you're creative, passionate about kubernetes and GPUs, and love having fun, please apply today! For two decades, we have pioneered visual computing, the art and science of computer graphics. With the invention of the GPU - the engine of modern visual computing - the field has expanded to encompass video games, movie production, product design, medical diagnosis and scientific research. Today, we stand at the beginning of the next era, the AI computing era, ignited by a new computing model, GPU deep learning. What you will be doing: You will be part of an DGX Cloud team responsible for production systems that enable large scalable GPU clusters to be used for a variety of AI workloads. This includes working on custom software related to scheduling GPU resources on kubernetes. Implementing monitoring and health management capabilities that enable industry leading reliability, availability, and scalability of GPU assets. You will be harnessing multiple data streams, ranging from GPU hardware diagnostics to cluster and network telemetry. Working with teams across NVIDIA to ensure production AI clusters run reliability and consistently with maximum performance. Evaluating system failures and improving services based on a well-defined incident management process. What we need to see: Direct experience in a software engineering role within a highly technical organization with demonstrable impact from your work. Software development experience with kubernetes APIs and frameworks not just operating a cluster. Highly motivated with strong communication skills, you can work successfully with multi-functional teams, principles, and architects and coordinate effectively across organizational boundaries and geographies. 15+ years in similar role and experience on large-scale production systems. Experience with common software engineering principles, tools and techniques. You possess a BS in Computer Science, Engineering, Physics, Mathematics or a comparable Degree or equivalent experience. Technical knowledge, including a systems programming language (Go, Python) and a solid understanding of data structures and algorithms. Ways to stand out from the crowd: Technical competency in managing and automating large-scale distributed systems independent of cloud providers. Advanced hands-on experience and deep understanding of cluster management systems (Kubernetes, Slurm, Bright Cluster Manager) Proven operational excellence in maintaining reliable and performant AI infrastructure. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people on the planet working for us. If you are creative and autonomous, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 3, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

NVIDIA is hiring experienced software engineers with kubernetes experience to help scale up its AI Infrastructure. We expect you to have significant software engineering experience with kubernetes including cluster operations, operator development, node health monitoring and working with GPU resource scheduling. We welcome out-of-the-box thinkers who can provide new ideas with strong execution bias. Expect to be constantly challenged, improving, and evolving for the better. You will help advance NVIDIA's capacity to build and deploy leading infrastructure solutions for a broad range of AI-based applications. If you're creative, passionate about kubernetes and GPUs, and love having fun, please apply today! For two decades, we have pioneered visual computing, the art and science of computer graphics. With the invention of the GPU - the engine of modern visual computing - the field has expanded to encompass video games, movie production, product design, medical diagnosis and scientific research. Today, we stand at the beginning of the next era, the AI computing era, ignited by a new computing model, GPU deep learning. What you will be doing: You will be part of an DGX Cloud team responsible for production systems that enable large scalable GPU clusters to be used for a variety of AI workloads. This includes working on custom software related to scheduling GPU resources on kubernetes. Implementing monitoring and health management capabilities that enable industry leading reliability, availability, and scalability of GPU assets. You will be harnessing multiple data streams, ranging from GPU hardware diagnostics to cluster and network telemetry. Working with teams across NVIDIA to ensure production AI clusters run reliability and consistently with maximum performance. Evaluating system failures and improving services based on a well-defined incident management process. What we need to see: Direct experience in a software engineering role within a highly technical organization with demonstrable impact from your work. Software development experience with kubernetes APIs and frameworks not just operating a cluster. Highly motivated with strong communication skills, you can work successfully with multi-functional teams, principles, and architects and coordinate effectively across organizational boundaries and geographies. 15+ years in similar role and experience on large-scale production systems. Experience with common software engineering principles, tools and techniques. You possess a BS in Computer Science, Engineering, Physics, Mathematics or a comparable Degree or equivalent experience. Technical knowledge, including a systems programming language (Go, Python) and a solid understanding of data structures and algorithms. Ways to stand out from the crowd: Technical competency in managing and automating large-scale distributed systems independent of cloud providers. Advanced hands-on experience and deep understanding of cluster management systems (Kubernetes, Slurm, Bright Cluster Manager) Proven operational excellence in maintaining reliable and performant AI infrastructure. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people on the planet working for us. If you are creative and autonomous, we want to hear from you! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 3, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

NVIDIA researchers depend on GPU clusters for large-scale AI workloads. Our DGX Cloud Kubernetes Runtime & Release team brings those clusters to life across major public clouds and specialized GPU providers, often on hardware that is new to the world when we get it. We build and maintain the supported Kubernetes runtime, automate its delivery, and bring new providers and GPU platforms into production. We’re growing quickly and taking on broader ownership of NVIDIA’s cluster software delivery. We’re hiring across Runtime, Release Engineering, and Provider Integration, with each role focused on your strengths. You don’t need experience across every area below. What you’ll be doing: Your primary focus will be one of three areas, with collaboration across the team: Runtime: Build Go controllers and APIs to install, upgrade, and validate GPU cluster software. Integrate components, define API contracts, and evolve Helm and Argo CD delivery toward controller-driven automation. Release Engineering: Build validation pipelines that inform release decisions across providers and GPU platforms. Develop systems to allocate GPU capacity across validation runs and account for cloud reservations and quotas. Make qualification more efficient through reusable tests and clear failure reports. Provider Integration: Bring new providers and GPU hardware into production, potentially among the first engineers working with new silicon. Resolve integration failures with partner teams and turn initial provisioning, upgrade, and operational checks into repeatable automation. What we need to see: 6+ years building production infrastructure software or distributed systems. Strong programming skills in Go or another language to build production systems, with willingness to work primarily in Go. Kubernetes experience and depth in at least one area: controllers and operators, release automation, test and validation systems, or cloud integration. Experience delivering engineering projects, diagnosing complex failures, and collaborating across teams. BS or MS in Computer Science, Engineering, or equivalent experience. Ways to stand out from the crowd: Experience in any of these areas is valuable, but not required: Go development with controller-runtime, CRDs, and reconcilers. Release qualification across multiple environments or platforms. GPU infrastructure, accelerated networking, or GPU scheduling. Bringing new hardware, regions, or cloud providers into production. Resource allocation, leasing, or fair-share scheduling and upstream integration, compatibility, or software supply chain integrity. This role suits an engineer who wants direct influence over what reaches production, and who builds for the hundredth cluster while shipping the first. Join us and help build the next generation of NVIDIA’s GPU cloud infrastructure! Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 3, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

NVIDIA is at the forefront of the generative AI revolution, building the software and systems that power the world’s most advanced large language model workloads. We are looking for a Software Engineer focused on bring-up, triage, benchmarking, analysis, and optimization of distributed training and inference workloads across NVIDIA GPU platforms at the largest scales we run. In this role you will help bring up, benchmark, and debug distributed LLM workloads on multi-GPU and multi-node deployments, and own the design and implementation of the benchmarking tooling, automation, and debugging workflows that support them. This is a hands-on role for an engineer who enjoys deep technical problems across deep learning systems, GPU performance, distributed computing, and large-scale operations. What you’ll be doing: Bring up, validate, and debug large-scale AI clusters, infrastructure, and end-to-end workloads. Bring up, tune, and benchmark AI pre-training, post-training, and inference workloads using PyTorch, NeMo / Megatron, TensorRT-LLM, and adjacent NVIDIA AI software stacks. Perform root-cause analysis of failures in large distributed environments Contribute to the resilience and failure-attribution tooling that detects, triages, and attributes node, fabric, and workload failures across the cluster. Build and maintain repeatable benchmark suites, automation, acceptance criteria, and qualification workflows on new platforms. Tune runtime settings, communication parameters, and deployment configurations in close partnership with framework, systems, and platform teams. Deliver actionable, data-driven recommendations based on profiling, benchmark results, and cluster characterization. What we need to see: Bachelor’s or Master’s in Computer Science or a related technical field (or equivalent experience). Experience developing software for AI, HPC, or systems-level applications. Hands-on experience with multi-GPU or multi-node workloads and CUDA-aware distributed execution. Background with debugging and scaling distributed systems. Experience debugging and triaging AI applications across the full stack, from the application level toward the hardware. Experience operating workloads in scheduled, containerized cluster environments. Excellent analytical, debugging, and communication skills, and a collaborative approach across teams. Strong Python and C/C++ programming skills. Ways to stand out from the crowd: Hands-on experience with NCCL and CUDA-aware distributed execution. Deep familiarity with the RDMA software stack (NCCL, IB verbs, UCX, libfabric) and with InfiniBand / RoCE congestion debugging. Experience building acceptance tests, benchmark harnesses, regression gates, or cluster qualification tooling for AI platforms, including MLPerf. Experience diagnosing performance jitter Experience building resilience, fault-detection, or failure-attribution systems for datacenter-scale infrastructure. NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you’re creative, autonomous, and love a challenge, we want to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 108,000 USD - 178,250 USD for Level 1, and 124,000 USD - 195,500 USD for Level 2. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until October 3, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.