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Nvidia Deep Learning Jobs in Virginia (NOW HIRING)

GRVTY

Expert ML Software Engineer

Springfield, VA · On-site

$150K - $165K/yr

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

GRVTY

Expert ML Software Engineer

Springfield, VA · On-site

$150K - $165K/yr

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

CoVar

Machine Learning Engineer

Mclean, VA · On-site

$83K - $111K/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 ...

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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 Sep 13, 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 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:

Nvidia Deep Learning jobs near you
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.

Senior Machine Learning Engineer, Radar & Remote Sensing

Chantilly, VA • On-site

Jobtailor
See more jobs at Jobtailorexternal

$128K - $177K/yr

Other

Posted 18 days ago

Job description

  • Own the radar and ML technical stack across radar/SAR simulation, machine learning, scientific software, and compute infrastructure
  • Act as a technical liaison between radar engineering, machine learning, software engineering, and operations teams
  • Develop and maintain radar and SAR simulation pipelines, including synthetic data generation, scene/return modeling, and validation workflows
  • Design, build, and refine end-to-end ML models and pipelines for radar-related tasks
  • Perform preprocessing, training, evaluation, and deployment-ready packaging
  • Utilize and analyze radar, 3D model, EO/IR, and sensing‑adjacent defense datasets
  • Create radar products and technical deliverables, including APIs, data schemas, containers, documentation, and integration guidance
  • Design, configure, and optimize local compute environments, GPU/eGPU setups, remote compute, storage, networking, containerization, and benchmarking
  • Support ML inference/training on constrained or embedded compute, including RFSoCs and FPGAs
  • Collaborate with RF/hardware partners on RF code processing, radar outputs, and deployable radar hardware productization
  • Help deploy and maintain web applications and internal tools on classified or restricted networks
  • Contribute to technical writing, SBIR proposals, and system documentation
Requirements
  • Active TS/SCI clearance
  • US Citizenship is required
  • Deep experience in Synthetic Aperture Radar, non-imaging radar, remote sensing, or signal processing
  • Solid understanding of radar/SAR fundamentals, including I/Q and complex‑valued data, simulation techniques, image formation algorithms and radar‑to‑image pipelines, and coherent vs. incoherent processing
  • Proven experience with radar or remote sensing simulations
  • Strong proficiency with scientific Python libraries, including NumPy, PyTorch, SciPy, Matplotlib, Jupyter, and related scientific stacks
  • Ability to build end-to-end ML pipelines encompassing data preprocessing, training, evaluation, versioning, packaging, and hand‑off to other engineers
  • Hands‑on experience with GPU compute, including PyTorch, CUDA, NVIDIA tooling, remote GPU servers, and local GPU compute
  • Ability to explain radar/ML concepts to non‑radar engineers and produce clear technical deliverables
  • Adherence to software engineering principles and best practices, including clean code, testing, and version control
  • Exceptional communication skills and ability to produce high‑quality technical documentation and deliverables
  • Prolonged periods sitting at a desk and working on a computer
  • Must be able to lift up to 10–15 pounds at a time
Core Competencies

Demonstrates expertise in Synthetic Aperture Radar and machine learning, with a strong ability to develop and maintain radar simulation pipelines and end-to-end ML models. Proficient in scientific Python libraries and GPU compute, with a focus on producing clear technical documentation and deliverables.

Highest‑signal resume keywords
  • Synthetic Aperture Radar Expertise
  • End‑to‑End ML Pipeline Development
  • Scientific Python Proficiency
  • GPU Compute Experience ATS Optimization Keywords Hard Skills
    • Radar Simulation
    • Machine Learning Models
    • Data Preprocessing
    • Image Formation Algorithms
    • Version Control
    • Testing Best Practices
    • Synthetic Data Generation
    • Scene/Return Modeling
    • Coherent Processing
    • Non‑Imaging Radar
    Soft Skills
    • Exceptional Communication Skills
    • Technical Writing
    Certifications & Qualifications
    • Active TS/SCI Clearance
    • US Citizenship
    Industry Keywords
    • Remote Sensing
    • Signal Processing
    • Radar Fundamentals
    • RF Code Processing
    • Embedded Compute
    • Technical Deliverables
    • SBIR Proposals
    • Compute Infrastructure
    • Data Schemas
    • Integration Guidance
    Tools & Technologies
    • NumPy
    • PyTorch
    • SciPy
    • Matplotlib
    • Jupyter
    • CUDA
    • NVIDIA Tooling
    • Remote GPU Servers
    • Containerization
    • APIs
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