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Nvidia Deep Learning Jobs in Virginia (NOW HIRING)

Create and train ML models for classification, regression, forecasting, or deep learning * Pipeline ... NVIDIA Triton Inference Server), GPU memory management, and batching/quantization tradeoffs

New

Our projects span multiple different data modalities and incorporate advanced algorithms, deep ... Experience running models on NVIDIA GPUs * Experience containerizing and deploying software using ...

Our projects span multiple different data modalities and incorporate advanced algorithms, deep ... Experience running models on NVIDIA GPUs * Experience containerizing and deploying software using ...

Our projects span multiple different data modalities and incorporate advanced algorithms, deep ... Experience running models on NVIDIA GPUs * Experience containerizing and deploying software using ...

Our projects span multiple different data modalities and incorporate advanced algorithms, deep ... Experience running models on NVIDIA GPUs * Experience containerizing and deploying software using ...

Create and train ML models for classification, regression, forecasting, or deep learning * Pipeline ... NVIDIA Triton Inference Server), GPU memory management, and batching/quantization tradeoffs

New

Create and train ML models for classification, regression, forecasting, or deep learning * Pipeline ... NVIDIA Triton Inference Server), GPU memory management, and batching/quantization tradeoffs

New

Experience with physics-based simulators such as NVIDIA's Issac Sim is required. * Robotics ... Knowledge and Learning: You possess broad technical interests along with a deep knowledge of a ...

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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 Aug 20, 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.

How much does a Nvidia Deep Learning engineer make?

A Nvidia Deep Learning engineer typically earns between $100,000 and $150,000 annually, depending on experience, location, and skill level. Senior roles or those with specialized expertise in AI frameworks and GPU programming can earn higher salaries, often exceeding $180,000. Compensation may also include bonuses and stock options in tech companies.

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 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 August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 28% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $83,166 per year, or $40 per hour.

Expert ML Software Engineer with Security Clearance

GRVTY

Springfield, VA • On-site

Other

Posted 2 days ago

New


Job description

What Impact You'll Have We are searching for a machine learning (ML) engineer in support of our customer in Springfield, VA. This ML Engineering role is onsite and combines software engineering and machine learning expertise. You will design, build, and maintain ML systems that learn from data to automate decision-making, such as predictive models, recommendation engines, or anomaly detection systems. What You'll be Owning * Model Development: Create and train ML models for classification, regression, forecasting, or deep learning * Pipeline Design: Build end-to-end ML pipelines for data preprocessing, feature engineering, model training, and evaluation * Deployment: Deploy models as APIs or backend services using frameworks like FastAPI, Flask, or Django * Monitoring & Maintenance: Track model performance, detect drift, and retrain with new data * Integration: Connect ML systems to applications, databases, and cloud platforms. What You Must Have * US Citizen with a TS/SCI Clearance and Ability to obtain a CI Polygraph * Bachelor's Degree in relevant field of study with 8 years of experience * Expert experience understanding of Kubernetes programming - Python is essential; with experience in Git, Linux, and REST APIs * Expert experience in Backend Development - API design, database integration, containerization (Docker) * Fundamental understanding of Math & Statistics - Linear algebra, probability, and/or calculus for ML theory * Experience with MLOps tooling and workflow orchestration - experiment tracking and model registry (MLflow or equivalent), and pipeline orchestration on Kubernetes (Kubeflow, Argo Workflows, or Airflow) - supporting automated retraining, model versioning, and drift detection * Expert experience developing ML algorithms - supervised/unsupervised learning, neural networks, and NLP - with hands-on expertise in at least one major framework (PyTorch or TensorFlow) and the broader Python ML stack (scikit-learn, pandas, NumPy) * Expertise in Data Engineering - Data cleaning, feature engineering, preventing data leakage What Would be Nice to Have * Master's Degree in relevant field of study with 6 years of experience * Experience serving models for low-latency inference at scale, including LLM serving frameworks (vLLM, TGI, or NVIDIA Triton Inference Server), GPU memory management, and batching/quantization tradeoffs * Experience with GPU orchestration and scheduling on Kubernetes in shared or multi-tenant clusters - NVIDIA Run:ai, KAI Scheduler, Kueue, Volcano, or Slurm. * Expert experience of Cloud & CI/CD DevOps Pipeline- AWS, Azure, Google Cloud ML services, SageMaker, Vertex AI