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Nvidia Hardware Engineer Jobs in Washington (NOW HIRING)

Via Logic LLC

Systems and Platform Engineer

Bethesda, MD · On-site

$150 - $190/hr

... hardware, GPU, and high-speed networking. In this role, you will design, develop, and optimize ... Experience in managing NVIDIA GPU data center platforms. (DGX, HGX, H200, H100, 200, B300, L40S)

Leidos

Systems and Platform Engineer

Bethesda, MD · On-site

... hardware, GPU, and high-speed networking. In this role, you will design, develop, and optimize ... Experience in managing NVIDIA GPU data center platforms. (DGX, HGX, H200, H100, 200, B300, L40S)

Leidos

Systems and Platform Engineer

Bethesda, MD · On-site

... hardware, GPU, and high-speed networking. In this role, you will design, develop, and optimize ... Experience in managing NVIDIA GPU data center platforms. (DGX, HGX, H200, H100, 200, B300, L40S)

Via Logic LLC

Senior AI Engineer

Rockville, MD · On-site

$130 - $160/hr

Familiarity with Azure/C2S, hardware platforms (CPUs, GPUs, FPGAs), and advanced analytical ... NVIDIA GPU ecosystems * Vector databases Commitment to Non‑Discrimination All qualified ...

VMD Corp

Systems Engineer - TS/SCI

Bethesda, MD · On-site

... hardware, Kubernetes, and NVIDIA GPU products. As a Systems Engineer - HPC & GPU Infrastructure, you will play a pivotal role in designing, developing, and optimizing GPU clusters for the IC ...

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Nvidia Hardware Engineer information

See Washington salary details

$57.8K

$165.6K

$222.6K

How much do nvidia hardware engineer jobs pay per year?

As of Sep 5, 2026, the average yearly pay for nvidia hardware engineer in Washington is $165,620.00, according to ZipRecruiter salary data. Most workers in this role earn between $139,900.00 and $184,600.00 per year, depending on experience, location, and employer.

What is an Nvidia hardware engineer?

An Nvidia Hardware Engineer is responsible for designing, developing, and optimizing cutting-edge hardware components such as GPUs, AI accelerators, and high-performance computing chips. They work on circuit design, system architecture, validation, and performance optimization to ensure Nvidia's products meet industry standards. These engineers collaborate with software teams to enhance hardware-software integration and improve efficiency. The role requires expertise in areas like VLSI design, FPGA/ASIC development, and power management. Strong problem-solving skills and a background in electrical or computer engineering are essential.

What does an Nvidia hardware engineer do?

A typical day for an Nvidia Hardware Engineer involves designing, simulating, and testing hardware components, often working closely with software, verification, and product teams to ensure designs meet performance and reliability standards. The role includes reviewing schematics, running validation tests, analyzing data, and addressing technical challenges as they arise. Engineers regularly participate in cross-functional meetings to align on project goals, share updates, and troubleshoot issues collaboratively. This collaborative environment ensures that new Nvidia products are developed efficiently and meet the industry’s demanding standards, providing engineers with significant opportunities for learning and professional growth.

What are the key skills and qualifications needed to thrive as an Nvidia hardware engineer?

To thrive as an Nvidia Hardware Engineer, you need a strong background in electrical engineering, digital and analog circuit design, and familiarity with ASIC/FPGA development, typically demonstrated through a relevant degree and experience. Expertise with industry-standard tools like Cadence, Synopsys, and scripting languages, as well as knowledge of hardware validation methods, is frequently required. Strong problem-solving abilities, teamwork, and effective communication help engineers navigate complex projects and work across multidisciplinary groups. These skills and qualities are critical for designing innovative, high-performance hardware components and ensuring their successful integration into Nvidia’s advanced technologies.

What are the most commonly searched types of Nvidia Hardware Engineer jobs in Washington?

The most popular types of Nvidia Hardware Engineer jobs in Washington are:

What are popular job titles related to Nvidia Hardware Engineer jobs in Washington?

For Nvidia Hardware Engineer jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Nvidia Hardware Engineer jobs in Washington look for?

The top searched job categories for Nvidia Hardware Engineer jobs in Washington are:

What cities in Washington are hiring for Nvidia Hardware Engineer jobs?

Cities in Washington with the most Nvidia Hardware Engineer job openings:

Nvidia Hardware Engineer jobs near you
Infographic showing various Nvidia Hardware Engineer job openings in Washington as of August 2026, with employment types broken down into 88% Full Time, 2% Part Time, 2% Temporary, and 8% Contract. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution, with an average salary of $165,620 per year, or $79.6 per hour.

AI Operations & Infrastructure Engineer

Invictus International Consulting, LLC

Fort George G Meade, MD • On-site

$175K - $200K/yr

Full-time

Re-posted 14 days ago

Job description

Title: AI Operations & Infrastructure EngineerLocation: Fort Meade, MDClearance: TS/SCI with a CI Polygraph Job Details:Manage and maintain AI computing platforms, including GPUs and other specialized hardware Install and configure GPU drivers and software Oversee the AI software stack and toolsImplement and manage containerization technologies like Docker and Kubernetes Configure and optimize networking infrastructure for AI workloads, including InfiniBand and Ethernet Manage storage solutions for AI data, considering performance and capacity requirementsDeploy and manage data processing units (DPUs) to accelerate data center workloads Monitor and manage AI cluster health and resource utilization Implement workload management and scheduling tools like Slurm and Kubernetes Ensure efficient power and cooling for AI infrastructure to maintain optimal operating conditionsConfigure high-performance networking solutions for AI and machine learning workloads Optimize network performance to ensure maximum throughput and minimal latency for AI computations Implement and fine-tune network protocols to enhance data transfer speeds and efficiency Integrate NVIDIA networking products with existing AI infrastructure, including servers, GPUs, and storage systems Deploy networking solutions in data centers to ensure seamless connectivity between AI components Diagnose and resolve networking issues impacting AI workloads to maintain optimal system performance Provide technical support and guidance to teams managing AI infrastructure Collaborate with data scientists, researchers, and IT professionals to understand networking requirements and challengesLead deployment and validation of servers and systems for AI enabled platformsConfigure and manage network topologies, BMC, OOB, TPM, power, and coolingInstall, upgrade, and validate GPU-based servers, BlueField DPUs, cables, and transceiversPerform firmware upgrades, hardware validation, and storage setup Configure and administer physical and logical resources, including M IG partitioning and BlueField platforms Install and configure operating systems, cluster software, drivers, containers (Docker), and NGC CLIManage and orchestrate clusters using NVIDIA Base Command Manager, Slurm, Pyxis, Enroot, and Run: AiPerform stress, benchmarking, and burn-in tests using HPL, NCCL, NVIDIA Nemo, and ClusterKit Verify cabling, firmware/software versions, and network signal quality Troubleshoot and resolve hardware, software, storage, and performance faults Replace faulty components and optimize systems for AMD/Intel platforms Monitor, document, and report on cluster health, resource usage, and job performance Ensure secure, efficient, and scalable operation of NVIDIA AI infrastructure, including user access and workload management Requirements:Qualified candidates must hold an active NVIDIA Professional Certification in either AI Networking, AI Infrastructure, or AI OperationsPrior direct, hands-on professional experience administering NVIDIA GPU and data processing unit (DPU) technologies, AI software stacks, and data center environments for high-performance AI workloadsComprehensive expertise in deploying and maintaining AI compute platforms, requiring proficiency in containerization and workload orchestration using Docker, Kubernetes, Slurm, NVIDIA Base Command Manager, and Run:AiMust be capable of configuring physical and logical resources, including Multi-Instance GPU (MIG) partitioning and BlueField platforms, while overseeing critical facility elements such as power, cooling, and storage solutionsThe ability to demonstrate advanced skills in AI networking, specifically configuring and optimizing high-performance InfiniBand and Ethernet fabrics to ensure maximum throughput and minimal latencyCurrent active TS/SCI clearance with a CI PolygraphEqual Opportunity Employer/Veterans/Disabled
Job Posted by ApplicantPro