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Nvidia Deep Learning Jobs in Virginia (NOW HIRING)

Senior Machine Learning Engineer

Mclean, VA

$105K - $145K/yr

About the Role We're seeking a Senior ML Engineer (Technical Lead) with deep hands-on expertise in ... Experiment tracking, Docker, ONNX/TensorRT, deploying inference services to the edge (e.g., NVIDIA ...

... algorithms and deep learning frameworks such as TensorFlow and PyTorch. • Demonstrated ... Nvidia GPU and C++ • Strong background developing / debugging • Experience in DevSecOps and ...

... and custom machine learning models. The AI Developer / Product SME will work closely with ... Provide deep expertise on LLMs, embedding models, fine-tuning techniques, agentic systems, and ...

AI Developer / Product SME

Mclean, VA · On-site

$145K - $185K/yr

... and custom machine learning models. The AI Developer / Product SME will work closely with ... Provide deep expertise on LLMs, embedding models, fine-tuning techniques, agentic systems, and ...

... and custom machine learning models. The AI Developer / Product SME will work closely with ... Provide deep expertise on LLMs, embedding models, fine-tuning techniques, agentic systems, and ...

From learning to leadership, this is your chance to take your career to the next level. Our Senior ... NVIDIA, Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure; one or more of ...

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Nvidia Deep Learning information

See Virginia salary details

$10.9K

$83.2K

$138.8K

How much do nvidia deep learning jobs pay per year?

As of Jul 9, 2026, the average yearly pay for nvidia deep learning in Virginia is $83,166.00, according to ZipRecruiter salary data. Most workers in this role earn between $71,400.00 and $137,800.00 per year, depending on experience, location, and employer.

What is an Nvidia Deep Learning job?

An Nvidia Deep Learning job typically involves working with AI, machine learning, and deep learning technologies to develop, optimize, and deploy neural network models. Employees in these roles may work on GPU acceleration, AI frameworks like TensorFlow and PyTorch, and specialized hardware like NVIDIA GPUs and TensorRT. Positions can range from research scientists and software engineers to AI infrastructure specialists, focusing on improving model performance and scalability. These professionals contribute to cutting-edge AI applications in fields like autonomous vehicles, healthcare, and robotics.

What are the main challenges faced by professionals working in Nvidia Deep Learning roles?

Professionals in Nvidia Deep Learning positions often encounter challenges such as optimizing deep learning models to run efficiently on GPU architectures, keeping up with rapidly evolving AI frameworks, and troubleshooting complex system-level integration issues. They may also need to balance tight project deadlines with the demands of rigorous research and experimentation. Collaboration with interdisciplinary teams—such as software developers, data scientists, and hardware engineers—is common and essential to deliver robust solutions. Overcoming these challenges helps professionals stay at the forefront of innovation in the AI and deep learning industry.

What are the key skills and qualifications needed to thrive in the Nvidia Deep Learning position, and why are they important?

Excelling in an Nvidia Deep Learning role requires a strong background in computer science, machine learning, and mathematics, often supported by an advanced degree in a related field. Expertise in deep learning frameworks (such as TensorFlow or PyTorch), CUDA programming, and experience with Nvidia GPU hardware are typically expected, along with relevant certifications like Nvidia Deep Learning Institute credentials. Strong analytical thinking, problem-solving abilities, and effective teamwork distinguish top performers in this position. These skills are crucial to efficiently develop, optimize, and deploy deep learning models leveraging Nvidia technologies in cutting-edge applications.

What are the most commonly searched types of Nvidia Deep Learning jobs in Virginia? The most popular types of Nvidia Deep Learning jobs in Virginia are:
What are popular job titles related to Nvidia Deep Learning jobs in Virginia? For Nvidia Deep Learning jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Nvidia Deep Learning jobs in Virginia look for? The top searched job categories for Nvidia Deep Learning jobs in Virginia are:
Infographic showing various Nvidia Deep Learning job openings in Virginia as of July 2026, with employment types broken down into 72% Full Time, 25% Part Time, and 3% Contract. Highlights an 72% Physical, 2% Hybrid, and 26% Remote job distribution, with an average salary of $83,166 per year, or $40 per hour.
AI Solutions Architect - FS or CI Polygraph Required

AI Solutions Architect - FS or CI Polygraph Required

Cloudera

Virginia Beach, VA • On-site

Other

PTO

This job post has expired 1 day ago. Applications are no longer accepted.


Job description

Business Area:

Professional Services

Seniority Level:

Mid-Senior level

Job Description:

At Cloudera, we empower people to transform complex data into clear and actionable insights. With as much data under management as the hyperscalers, we're the preferred data partner for the top companies in almost every industry. Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world's largest enterprises.

As an AI Solutions Engineer within Cloudera's Public Sector Consulting team, you will be the technical architect and execution lead for agencies moving from "data chaos" to "agentic autonomy." You will work directly with government organizations to design, build, and deploy mission-critical AI applications on the Cloudera Data Platform (CDP).

This is not a "theoretical" role. You will be on the front lines of Phase 2 and Phase 3 adoption journeys-helping customers clean legacy data silos, select the right model architectures, and industrialize MLOps pipelines in highly secure, often air-gapped or hybrid-cloud environments.

As the AI Solutions Engineer you will:

1. AI Model Strategy, Selection and Implementation

  • Evaluate and select optimal model architectures (LLMs, SLMs, or traditional ML) based on mission requirements, considering tradeoffs between accuracy, latency, and cost.

  • Guide customers on "Build vs. Buy vs. Fine-tune" decisions, prioritizing open-source models (Llama, Mistral, Falcon) that can run securely within a sovereign data perimeter.

  • Experience building Agentic Workflows (AI agents that can execute API calls and multi-step tasks).

2. End-to-End Data Engineering

  • Design and implement robust data pipelines within CDP to transform "messy" legacy data into AI-ready formats.

  • Develop and optimize Vector Databases and Retrieval-Augmented Generation (RAG) architectures to ground AI responses in verified agency facts.

  • Build Data pipelines with Spark, Nifi, Kafka or other ETL tools.

3. Optimization & Performance Tuning

  • Optimize model inference for production environments using quantization, pruning, and hardware acceleration (NVIDIA GPU orchestration).

  • Implement LLMOps to monitor model performance, detect hallucination rates, and manage model versioning and drift.

4. Public Sector Advisory & Governance

  • Collaborate with the customer's AI Center of Excellence (CoE) to establish automated guardrails for ethics, bias mitigation, and FedRAMP/IL5 compliance.

  • Translate complex technical AI concepts into mission-value briefings for GS-level stakeholders and agency leadership.

We're excited about you if you have: (Minimum Qualifications):

  • Experience: 5+ years in Data Engineering, Machine Learning, or Software Engineering, with at least 2 years focused on Generative AI or Deep Learning.

  • Technical Stack: Expertise in Python and deep learning frameworks (PyTorch, TensorFlow, Hugging Face).

    • Hands-on experience with Cloudera (CDP), Spark, or similar big data ecosystems.

    • Proficiency in orchestration tools like LangChain, LlamaIndex, or Haystack.

    • Experience developing visual data representations and dashboards (Django, React, or Angular)

    • Experience using a compiled programming language, preferably one that runs on the JVM (Java, Scala, etc)

  • Data Expertise: Proven ability to build ETL/ELT pipelines and work with both SQL and NoSQL/Vector databases (e.g., Pinecone, Milvus, or PGVector).

  • Public Sector Knowledge: Understanding of government security frameworks (NIST AI RMF, FedRAMP, SRGs, STIGs).

  • Active Top Secret Security Clearance

You may also have: (Preferred Qualifications)

  • Experience fine-tuning of foundational models using techniques such as PEFT (Parameter-Efficient Fine-Tuning) and LoRA to adapt AI to domain-specific government nomenclature.

  • Experience training of specialized models on proprietary datasets while ensuring strict adherence to data privacy and sensitivity labels.

  • Experience installing and operating Cloudera Data Platform

  • Experience installing and operating Kubernetes

  • Experience in Air-Gapped deployments and managing AI workloads in disconnected environments.

  • Advanced degree (MS or PhD) in Computer Science, Data Science, or a related field.

  • Active Counterintelligence (CI) or Full Scope (FS) Poly is required.

This role is not eligible for immigration sponsorship.

What you can expect from us:

  • Generous PTO Policy

  • Support work life balance with Unplugged Days

  • Flexible WFH Policy

  • Mental & Physical Wellness programs

  • Phone and Internet Reimbursement program

  • Access to Continued Career Development

  • Comprehensive Benefits and Competitive Packages

  • Paid Volunteer Time

  • Employee Resource Groups

EEO/VEVRAA

#LI-Remote

#LI-ND3