Certified Instructor Program

Learn what it takes to become an NVIDIA-Certified Instructor.

Program Overview

The NVIDIA Certified Instructor Program (CIP) enables candidates to become certified to teach NVIDIA workshops. The program connects qualified instructors with high-quality training and hands-on course materials, lab access, and fully configured, GPU-accelerated workstations in the cloud. Through this program, candidates can be certified to teach workshops offered by:

  • Deep Learning Institute (DLI): Provides training designed for those who build AI applications, including developers, data scientists, and software engineers.
  • NVIDIA Academy: Provides training designed for those who build, deploy, operate, and maintain AI infrastructure, including system administrators and network engineers.

General Qualifications

To qualify for this program, candidates must fit into one of the following categories:

  • Associated with an organization that is an NVIDIA Authorized Learning Partner or Education Services Partner
  • Employed by NVIDIA 
  • For DLI Workshops only: Currently employed by an academic institution that qualifies for the DLI University Ambassador Program. Candidates in this category must:
    • Be a current faculty or a full-time lecturer at an academic institution
    • Teach at least two workshops every 12 months to at least 40 total students.

Note that teaching assistants, postdocs, and students at any level (undergraduate, graduate, or PhD) do not qualify.

In addition, candidates must meet the technical qualifications listed by workshop or for the selected Learning Path as this is not a train-the-trainer program.

Consideration for acceptance into the program is based on: 

  • Teaching experience
  • Expertise in Learning Path topics covered in workshops
  • Satisfaction of certification exam prerequisites 
  • Agreement to the terms in the Certified Instructor Agreement

Instructor Certification Process for Developer Workshops

Review DLI Learning Path Qualifications

The program is organized in Learning Paths focused on specific core themes.  This provides certified instructors with a foundational framework mapped to essential competencies within each Learning Path. Select the Learning Path you are interested in and verify that you meet the qualifications to teach the workshops within the Learning Path. 

New Learning Paths are being developed for Enterprise AI and Physical AI--sign up for NVIDIA Training news to stay informed on their availability.

Candidates must demonstrate experience developing at least one significant agentic AI application in a commercial, academic, or open-source capacity and be able to explain their contributions, technical decisions, and results.

Candidates should have experience with:

  • Advanced Python and applied software engineering for modular AI applications.
  • Designing stateful agents with reasoning, memory, tool calling, routing, and task execution.
  • Managing context, dialog, and control flow across multi-step interactions.
  • Connecting agents to retrieval systems, APIs, external tools, and data sources.
  • Selecting orchestration patterns for sequential, branching, and event-driven workflows.
  • Preventing agent derailment through validation, guardrails, retries, and human oversight.
  • Coordinating specialized agents through delegation, communication, and shared-state management.
  • Evaluating task completion, tool selection, reliability, safety, and failure recovery.
  • Understanding the capabilities and trade-offs of different agent architectures and orchestration frameworks.

Experience with LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, Hugging Face, or similar technologies is beneficial, but candidates should primarily demonstrate knowledge of the underlying agentic concepts rather than a specific framework.

Apply

University Ambassador applications are by invitation only. Candidates must be nominated by an NVIDIA sponsor.

 Authorized Learning Partner applicants and NVIDIANs should reach out to the DLI team directly for intake.

Get Certified

DLI Instructor Certification features a Learning Path-based framework. Successful candidates will be certified to teach all workshops within their selected DLI Learning Path.

Accepted candidates will be placed into a cohort and given three months to complete the following steps:

  • Submit a sample teaching video
  • Complete student assessments for all workshops within the Learning Path
  • Optionally, review recordings of the workshops within the Learning Path
  • Pass the designated Professional-level NVIDIA Certification exam for the Learning Path
  • Pass a 1:1 interview with a DLI Principal Instructor

Start Teaching

Once you have become certified, you can now start teaching.

To schedule a workshop, log into the Certified Instructor Portal and submit the Training Services Request form.

Get Certified in an Additional Learning Path

Take your skills to the next level by earning instructor certification in additional Learning Paths. Broaden your teaching portfolio, unlock new professional opportunities, and deepen your mastery of cutting-edge topics. A new application is required for each Learning Path.

Maintain Your Certified Status

To maintain your status, certified instructors are required to:

  • Attend mandatory professional development (annually)
  • Attend mandatory content briefings for all workshop/Learning Path updates
  • Optional attendance at quarterly regional meet-ups (virtual)
  • Maintain positive student feedback ratings
  • Renew membership annually
  • Adhere to the guidelines detailed in the Certified Instructor Agreement
  • Certified Instructors are required to deliver at least two NVIDIA workshops per year, to at least 40 total students total

Instructor Certification Process for AI Infrastructure Workshops

Review Academy Workshop Qualifications

Pass Instructor Candidate Interview

The next step in the certification process is to complete an interview with the NVIDIA Academy team. This interview evaluates your technical background, instructional experience, and readiness to become an NVIDIA Certified Instructor.

Duration: 1 hour

Presentation: Candidates will deliver a 30-minute presentation on a technical topic of their choice.

Assessment: The interview also includes an evaluation of the candidate’s technical knowledge, communication skills, and overall proficiency.

Interviews are conducted by invitation only. Eligible candidates will be contacted directly by NVIDIA to schedule their interview.

Complete Training and Technical Certifications

Before becoming certified to teach NVIDIA Academy courses, candidates must complete training and have their skills validated via NVIDIA certification exams. 

Step 1: Complete the Required Fundamentals Training and Certification

  1. Complete the self-paced Course: AI Infrastructure and Operations Fundamentals
  2. Pass the Associate Certification Exam: NCA-AIIO

Step 2: Complete the Workshop Your Wish to Teach 

  1. AI Infrastructure Professional Public Workshop
  2. AI Operations Professional Public Workshop 
  3. Cumulus Linux Administration Public Workshop
  4. SpectrumX Networking Platform Administration Private Workshop 
  5. NVIDIA AI Enterprise on Bare-Metal Kubernetes Public Workshop

Step 3: Earn Professional Certifications (Where Applicable)

Some workshops also require candidates to pass the related professional certification exams:

  1. For the AI Infrastructure workshop, pass the Professional Certification Exam: NCP-AII 
  2. For the AI Operations workshop, pass the Professional Certification Exam: NCP-AIO 

Step 4: Complete Course Immersion and Shadowing

Candidates must become familiar with the course materials, delivery environment, and instructional approach before certification. This includes: 

  • Course Immersion
    • Master the course slides, lab guides, and overall content flow
    • Practice using the NVIDIA Academy platform for lab delivery
    • Develop troubleshooting and student support skills
  • Shadowing
    • Assist with an instructor-led training session
    • Observe experienced instructors in action
    • Study instructional delivery style and pacing techniques
    • Learn effective learner interaction strategies
    • Understand classroom management approaches

Get Certified as an Instructor

In this final step to become a NVIDIA Certified Instructor (NCI), candidates must successfully complete a multi-part assessment process based on industry-standard instructor certification practices. Work with your NVIDIA contact to schedule these activities. 

Teach-Back Session (45–60 minutes)
Candidates deliver an assigned technical topic to demonstrate instructional readiness and presentation skills. Evaluation areas include:

  • Technical accuracy and depth of knowledge
  • Clarity, structure, and organization of content
  • Delivery style and learner engagement
  • Time management and pacing
  • Ability to confidently answer learner questions

Lab Proficiency Assessment (3–4 hours)
Candidates complete a series of hands-on lab exercises to demonstrate: 

  • Practical understanding of course technologies
  • Troubleshooting and problem-solving skills
  • Ability to guide learners through complex technical scenarios

Supervised First Delivery
Candidates deliver their first course with support from an experienced instructor through mentor oversight, or a co-delivery teaching model. After the course delivery, candidates receive:

  • Learner feedback analysis
  • Structured mentor coaching and feedback
  • A personalized development plan for continued improvement

Get Certified in Additional Workshops

Take your skills to the next level by earning certifications in additional NVIDIA workshops. Broaden your teaching portfolio, unlock new professional opportunities, and deepen your mastery of cutting-edge AI infrastructure technologies. 

Maintain Your Certified Instructor Status

To maintain your status, certified instructors are required to:

  • Maintain student positive feedback ratings.
  • Maintain active technical certifications (renewed per NVIDIA requirements) 
  • Deliver a minimum number of courses per year
  • Participate in mandatory refresher training and professional development
  • Adhere to the guidelines detailed in the Certified Instructor Agreement. 

Additional Resources

List of All DLI Workshops

View the latest list of developer workshops. The list can be filtered by topic.

List of All Academy Workshops

View the latest list of all AI infrastructure workshops. The list can be filtered by topic.

NVIDIA Certified Instructor Directory

Looking to partner with a University Ambassador for an upcoming workshop?

Get Started Today

View our learning paths and select either your first or your next workshop to get certified to teach.

Questions?

Reach out if you have questions about our certified instructor program.

Stay Informed

Get training news, announcements, and more from NVIDIA, including the latest information on new self-paced courses, instructor-led workshops, free training, discounts, and more. You can unsubscribe at any time.

Adding New Knowledge to LLMs

Candidates must demonstrate experience working on at least one LLM application involving finetuning, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:

  • A professional role (e.g. engineer, data scientist)
  • A completed project
  • Academic coursework

Candidates should have experience with the following:

  • Differentiating RAG, finetuning, and alignment
  • Strategies to drive creation of diverse synthetic data sets
  • Parameter efficient finetuning
  • Pruning techniques
  • Distillation techniques
  • LLM decoding strategies, such as top-k/p, beam search, etc.
  • LLM output evaluation techniques, such as ROUGE/BLEU, semantic similarity, LLM-as-a-judge, etc.

Candidates must also demonstrate teaching experience, such as:

  • Classroom teaching experience
  • Significant presentation experience

Building Agentic AI Applications with LLMs

Candidates must demonstrate experience working on at least one agentic AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience could include:

  • A professional role (e.g. engineer, data scientist)
  • A completed project
  • Academic coursework

Candidates should have experience with the following:

  • Modern LangChain, including LCEL, LangGraph, etc.
  • Differentiating capabilities of various agentic tools such as LangGraph, CrewAI, Autogen, etc.
  • Stateful LLM systems
  • LLM tool calling
  • Strategies to prevent derailing
  • Agentic routing

Candidates must also demonstrate teaching experience, such as:

  • Classroom teaching experience
  • Significant presentation experience

Building RAG Agents with LLMs

Candidates must demonstrate significant experience in data science, machine learning, deep learning, and the telecommunications industry, having worked on at least one significant AI application, either in a commercial or academic capacity, and explain their work. Qualifying experience includes:

  • Active open-source contribution or coordination efforts in the area
  • Experience orchestrating dialog management and information retrieval systems
  • Strong applied software engineering expertise, esp. surrounding microservices and inference server solutions

Candidates should have the following:

  • Strong proficiency in Python, including functional programming and server deployment
  • Expertise in large language models as inference endpoints, including industry use-cases.
  • Strong experience with modern LangChain (including LCEL) and LangServe required; understanding of LangGraph, LlamaIndex, Langsmith, and NeMo Guardrails useful.
  • Experience with microservice/server orchestration, including Docker and FastAPI.
  • Experience with modern RAG, including some derivative formulations and pros/cons.
  • Understanding of agentic behavior, tooling, and modular agent components.
  • Intuition of evaluation metrics and performance expectations.

Candidates must also demonstrate teaching experience, such as:

  • Classroom teaching experience
  • Significant presentation experience

AI Infrastructure

Candidates must demonstrate comprehensive, up-to-date expertise in deploying and managing AI data center infrastructure, including compute, networking, storage, and virtualization. Ideal candidates have hands-on experience with advanced AI infrastructure technologies and workflows. 

Practical Experience: 

  • Professional roles such as data center administrator, DevOps engineer, system administrator, or AI infrastructure engineer working with enterprise-scale AI environments
  • Direct, practical experiencewith
  • Deploying and managing AI compute platforms (GPUs, CPUs, DPUs) 
  • Storage architecture and performance optimization for AI data centers 
  • Virtualization technologies (VMs, containers, GPU partitioning with vGPU/MIG) 
  • Installing and managing NVIDIA software (GPU drivers, DOCA, NVIDIA NGC containers, NVIDIA AI Enterprise Suite) 
  • Using management tools such as NVIDIA Base Command Manager (BCM) for AI cluster provisioning and operations 


Knowledge and Expertise:

  • Networking: knowledge of Ethernet and InfiniBand, switching, routing, and advanced data center networking automation 
  • Linux: Proficiency in Linux system administration (user management, configuration, troubleshooting) 
  • Storage: Understanding of file systems, storage protocols, and performance testing 
  • Virtualization: Experience with VMs, containers, and GPU virtualization 
  • AI Concepts: Familiarity with machine learning, deep learning, and common AI applications

AI Operations

Candidates must demonstrate comprehensive, up-to-date expertise in operating and managing AI data center environments, including compute, networking, storage, and virtualization. Ideal candidates have hands-on experience with advanced AI data center operations and workflows. 

Practical Experience: 

  • Professional roles such as data center administrator, DevOps engineer, system administrator, AI infrastructure engineer, or data scientist working with enterprise-scale AI environments 
  • Direct, practical experience with: 
  • Operating and managing AI compute platforms (GPUs, CPUs, DPUs) 
  • Provisioning and managing AI workloads and virtualization in data centers 
  • Maintaining InfiniBand and Ethernet networks for AI workloads 
  • Storage architecture and performance optimization for AI data centers 
  • Virtualization technologies (VMs, containers, GPU partitioning) 
  • Installing and managing NVIDIA software (GPU drivers, DOCA, NGC containers, AI Enterprise Suite) 
  • Using management tools such as NVIDIA DCGM, UFM, and BlueField management utilities 


Knowledge and Expertise:

  • Networking: knowledge of Ethernet and InfiniBand, switching, routing, and data center networking automation 
  • Linux: Proficiency in Linux system administration (user management, configuration, troubleshooting) 
  • Storage: Understanding of file systems, storage protocols, and performance testing 
  • Virtualization: Experience with VMs, containers, and GPU virtualization 
  • AI Concepts: Familiarity with machine learning, deep learning, and common AI applications

Cumulus Linux Administration

Candidates must demonstrate comprehensive, up-to-date expertise in data center networking. Ideal candidates have hands-on expertise in advanced AI networking technologies and real-time monitoring.

Practical Experience: 

  • Professional experience (e.g., network engineer, system administrator, infrastructure engineer, solutions architect, DevOps, trainers) deploying, configuring, and managing Cumulus Linux-based network environments in production data centers.
  • Experience with NVIDIA Air cluster simulation and deployment.


Knowledge and Expertise:

  • Proficiency in Linux administration (shell, config management, troubleshooting) 
  • Strong knowledge of Ethernet networking, switching, and routing 
  • NVIDIA networking hardware experience preferred 
  • Layer 2 and Layer 3 networking: VLANs, bridging, trunking, link aggregation (LAG/MLAG), SVIs, VRR, VRF, and BGP (including BGP unnumbered)
  • Network virtualization using VXLAN and EVPN, including both symmetric and asymmetric routing models 
  • Experience with network automation tools and workflows (e.g., Ansible, REST APIs, Zero Touch Provisioning)
  • Monitoring, diagnostics, and troubleshooting across multiple network layers, including hardware resource monitoring and OpenTelemetry 


Preferred: 

  • NVIDIA Cumulus Linux course certificate or NCP-AIN certification (NVIDIA Certified Professional AI Networking)
  • Active involvement in open-source networking projects or community

Spectrum-X Networking Platform Administration

Candidates must demonstrate comprehensive, up-to-date expertise in data center networking. Ideal candidates have hands-on expertise in advanced AI networking technologies and real-time monitoring. 

Practical Experience: 

  • Professional roles such as network engineer, DevOps, technical instructors, or system administrators working with Spectrum-X, Cumulus Linux, and AI data center networks 
  • Cumulus Linux Certified Instructor 
  • Experience with NVIDIA Air cluster simulation and deployment 
  • Real-time monitoring and troubleshooting with NVIDIA NetQ, Cumulus Linux CLI, and telemetry tools (ASIC, OTLP, DTS) 


Knowledge and Expertise:

  • Cumulus Linux: Hands-on experience with installation, configuration, upgrades, Layer 2/3 features, network virtualization (VXLAN/EVPN), automation, and troubleshooting 
  • Networking: Strong knowledge of Ethernet, switching, routing, and data center networking automation 
  • Linux: Proficiency in Linux administration and managing Linux-based network environments 


Preferred:

  • NVIDIA Cumulus Linux course certificate or NCP-AIN certification (NVIDIA Certified Professional AI Networking)

  • Active involvement in open-source networking projects or community

NVIDIA AI Enterprise on Bare-Metal Kubernetes Deployment

Candidates must demonstrate solid expertise in enterprise AI infrastructure, with hands-on experience deploying Kubernetes-based workloads on bare-metal servers and managing NVIDIA GPU environments at scale.

Practical Experience:

  • IT professional with hands-on experience in enterprise data center environments (servers, storage, networking, GPUs, Linux)
  • Experience deploying and managing Kubernetes clusters on bare-metal infrastructure
  • Hands-on experience with containerization: Container and Kubernetes (CNCF)
  • Experience with GPU-enabled workloads and NVIDIA GPU configuration in production
  • Familiarity with NVIDIA AI Enterprise suite components (NIM, GPU Operator, DCGM Exporter)
  • Experience deploying AI/ML workloads, preferably including RAG pipelines


Knowledge and Expertise:

  • Data center infrastructure: servers, storage, networking, GPUs, and Linux operating systems
  • Kubernetes cluster management and Helm chart deployments
  • NVIDIA GPU Operator and GPU configuration on Kubernetes
  • Monitoring and observability: DCGM Exporter, Prometheus, Grafana
  • Linux system administration and bare-metal server management
  • Basic understanding of AI/ML concepts and enterprise AI workflows


Preferred:

  • NCA-AIIO certification