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Nvidia Engineering Jobs in Maryland (NOW HIRING)

Phase2 Technology

$112.80 - $257/hr

Collaborate across engineering, data, and AI teams to align infrastructure solutions with business ... Experience working with NVIDIA GPU technologies, including CUDA, NCCL, TensorRT, and DGX systems ...

Belay Technologies

Mid-Level Software Engineer

Columbia, MD · On-site

$170K - $201K/yr

Partner directly with contract engineering teams to optimize system performance and user workflows ... particularly NVIDIA enterprise class hardware * Demonstrated ability to document technical ...

Belay Technologies

Mid-Level Software Engineer

Columbia, MD · On-site

$171K - $203K/yr

Partner directly with contract engineering teams to optimize system performance and user workflows ... particularly NVIDIA enterprise class hardware * Demonstrated ability to document technical ...

Belay Technologies

Mid-Level Software Engineer

Columbia, MD · On-site

$171K - $203K/yr

Partner directly with contract engineering teams to optimize system performance and user workflows ... particularly NVIDIA enterprise class hardware * Demonstrated ability to document technical ...

Leidos

Systems and Platform Engineer

Bethesda, MD · On-site

Kubernetes Cluster Engineering: Design, configure, and maintain enterprise Kubernetes platforms ... Experience in managing NVIDIA GPU data center platforms. (DGX, HGX, H200, H100, 200, B300, L40S)

Leidos

Systems and Platform Engineer

Bethesda, MD · On-site

Kubernetes Cluster Engineering: Design, configure, and maintain enterprise Kubernetes platforms ... Experience in managing NVIDIA GPU data center platforms. (DGX, HGX, H200, H100, 200, B300, L40S)

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Nvidia Engineering information

What is an Nvidia engineer?

An Nvidia Engineering job involves designing, developing, and optimizing hardware or software solutions in areas such as graphics processing, AI, and high-performance computing. Engineers at Nvidia work on cutting-edge technologies, including GPUs, deep learning frameworks, and system architecture. Roles vary from hardware design and verification to software development and AI research, depending on expertise. Strong skills in programming, computer architecture, and problem-solving are typically required.

What types of projects do Nvidia engineers typically work on, and how is teamwork structured within the engineering department?

Nvidia Engineers commonly engage in projects related to GPU development, AI and deep learning solutions, software driver optimization, and next-generation hardware innovation. Project teams are often multidisciplinary, bringing together software, hardware, and systems engineers to collaborate closely on end-to-end product development. Engineers frequently work in agile, fast-paced environments, attend regular team stand-ups, and participate in cross-functional meetings. This collaborative structure fosters creativity, accelerates problem-solving, and ensures high-quality product delivery while offering team members exposure to diverse technologies and career growth opportunities.

What are the key skills and qualifications needed to thrive as an Nvidia engineer, and why are they important?

To thrive in Nvidia Engineering, candidates typically need strong proficiency in computer engineering, software development, and a solid understanding of hardware architecture, often backed by a relevant degree such as Electrical Engineering or Computer Science. Familiarity with tools like CUDA, C/C++, Python, and version control systems, as well as experience with GPU programming, are highly valued, and certifications such as Nvidia's Deep Learning Institute credentials can enhance a candidate's profile. Excellent problem-solving, team collaboration, and communication skills set top performers apart in this role. These skills and qualifications enable engineers to contribute effectively to complex, innovative projects that drive Nvidia's technological advancements.

What are the most commonly searched types of Nvidia Engineering jobs in Maryland?

The most popular types of Nvidia Engineering jobs in Maryland are:

What are popular job titles related to Nvidia Engineering jobs in Maryland?

For Nvidia Engineering jobs in Maryland, the most frequently searched job titles are:

What job categories do people searching Nvidia Engineering jobs in Maryland look for?

The top searched job categories for Nvidia Engineering jobs in Maryland are:

What cities in Maryland are hiring for Nvidia Engineering jobs?

Cities in Maryland with the most Nvidia Engineering job openings:

Nvidia Engineering jobs near you
Infographic showing various Nvidia Engineering job openings in Maryland as of August 2026, with employment types broken down into 89% Full Time, 7% Part Time, 3% Contract, and 1% Nights. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution.

AI Operations & Infrastructure Engineer

Invictus International Consulting

Fort George G Meade, MD

$119K - $157K/yr

Full-time

Re-posted 26 days ago

Job description

Title: AI Operations & Infrastructure Engineer

Location: Fort Meade, MD

Clearance: 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 tools
  • Implement 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 requirements
  • Deploy 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 conditions
  • Configure 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 challenges
  • Lead deployment and validation of servers and systems for AI enabled platforms
  • Configure and manage network topologies, BMC, OOB, TPM, power, and cooling
  • Install, upgrade, and validate GPU-based servers, BlueField DPUs, cables, and transceivers
  • Perform 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 CLI
  • Manage and orchestrate clusters using NVIDIA Base Command Manager, Slurm, Pyxis, Enroot, and Run: Ai
  • Perform 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 Operations
  • Prior 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 workloads
  • Comprehensive 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:Ai
  • Must 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 solutions
  • The ability to demonstrate advanced skills in AI networking, specifically configuring and optimizing high-performance InfiniBand and Ethernet fabrics to ensure maximum throughput and minimal latency
  • Current active TS/SCI clearance with a CI Polygraph

Equal Opportunity Employer/Veterans/Disabled

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