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Nvidia Deep Learning Jobs in Virginia (NOW HIRING)

Navstar

Machine Learning Engineer

Centreville, VA

$102K - $144K/yr

... a deep learning framework, preferably PyTorch * Proficiency with basic libraries for machine learning such as, Open-CV, scikit-learn, and pandas * Familiarity with Linux * Familiarity with Nvidia ...

CRI

Video Analytics Engineer

Sterling, VA · On-site

... deep learning models to analyze video data, often focusing on object detection, tracking, and ... Experience with Linux systems, networking protocols, and edge devices (e.g., NVIDIA Jetson ...

CoVar

Senior Machine Learning Engineer

Mclean, VA

$107K - $147K/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 ...

CoVar

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 ...

Arcfield

Lead Edge AI/ML Engineer

Richmond, VA · On-site +1

$101K - $133K/yr

Lead Edge AI / Machine Learning Engineer Strategic Technology Consulting (STC), an Arcfield Company ... The ideal candidate will bring deep experience moving AI/ML beyond prototype environments and into ...

Steampunk

AI Developer

Mclean, VA · On-site +1

$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 ...

Steampunk

AI Developer / Product SME

Mclean, VA

$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 ...

Steampunk

AI Developer

Mclean, VA

$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 ...

Steampunk

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 ...

Steampunk

AI Developer

Mclean, VA

$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 ...

Steampunk

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 ...

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All JobsNvidia Deep Learning JobsNvidia Deep Learning Jobs in Virginia

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 Jun 11, 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.

How much does a NVIDIA deep learning performance architect make?

A NVIDIA deep learning performance architect typically earns between $120,000 and $180,000 annually, depending on experience, location, and specific responsibilities. The role often requires expertise in AI frameworks, GPU architecture, and performance optimization. Compensation may also include bonuses and stock options based on performance and company policies.

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.

Is ML a high paying job?

Machine learning (ML) roles, including those related to Nvidia deep learning, are generally well-paid due to high demand for specialized skills in AI, data analysis, and programming. Salaries vary based on experience, location, and certifications, but many ML positions offer competitive compensation compared to other tech roles.

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.

How much does a deep learning engineer make at NVIDIA?

A deep learning engineer at NVIDIA 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 and GPU programming can earn higher salaries, often exceeding $180,000. Compensation may also include bonuses and stock options.

How hard is it to get hired at NVIDIA?

Getting hired at NVIDIA for deep learning roles can be competitive, often requiring strong technical skills in machine learning, deep learning frameworks, and programming languages like Python and C++. Candidates typically need relevant experience, a solid educational background, and a demonstrated ability to work on complex projects. The hiring process may include technical interviews, coding assessments, and behavioral evaluations.

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:
Nvidia Deep Learning jobs near you
Infographic showing various Nvidia Deep Learning job openings in Virginia as of June 2026, with employment types broken down into 72% Full Time, and 28% Contract. Highlights an 100% In-person job distribution, with an average salary of $83,166 per year, or $40 per hour.
Senior Wireless Machine Learning Engineer, AI-RAN

Senior Wireless Machine Learning Engineer, AI-RAN

DeepSig, Inc

Arlington, VA • On-site

$120K - $165K/yr

Full-time

Posted 25 days ago

Job description

Job Type
Full-time
Description
Type: Full-Time(W2) On-site/Hybrid, Arlington, VA (Remote option available for the right candidate)
DeepSig is defining the future of wireless communications by merging deep learning with the Radio Access Network (RAN). We are seeking an experienced Technical Lead to architect and drive the development of our next-generation AI-native RAN.
In this role, you will design, prototype, and validate novel AI/ML components-such as neural receivers, neural beamforming, neural scheduling, digital twin, and ISAC (Integrated Sensing and Communications)-that outperform traditional signal processing methods. You will work at the cutting edge of 6G innovation, taking concepts from mathematical intuition to simulation (e.g. NVIDIA Sionna) and real-time implementation.
What You'll be Doing
  • Applied AI Research: Design and train modern deep learning models (Transformers, Vision architectures, etc.) to solve complex physical layer problems, including channel estimation, MIMO detection, and beam management
  • Simulation & Validation: Build high-fidelity link-level simulations using NVIDIA Sionna and ray-tracing to train, test, and benchmark AI models against legacy 5G baselines
  • Prototyping & Deployment: Transition research models into deployable "dApps" for the Distributed Unit (DU), optimizing inference for latency and compute efficiency on NVIDIA GPUs
  • New Capabilities: Explore emerging AI-RAN frontiers such as Integrated Sensing and Communications (ISAC), neural scheduling, and channel digital twins
  • Innovation & IPR: Drive technical innovation by authoring invention disclosures, filing patents, and generating technical reports to support our standardization team in 3GPP and O-RAN Alliance contributions
  • Data Engineering: Architect data pipelines for generating synthetic training datasets and developing "Sim-to-Real" transfer techniques to ensure robust performance in real-world networks

Required Qualifications
  • Education: Ph.D. or Master's in Computer Science, Electrical Engineering, or Applied Mathematics with a focus on Deep Learning and/or Communications Systems
  • AI/ML Expertise: 3+ years of experience designing and training deep neural networks from scratch. Strong grasp of modern architectures and optimization techniques
  • Applied Signal Processing: Experience applying machine learning to real-time time-series data, signal processing, or physics-based problems (Audio, RF, or similar domains)
  • Research to Code: Proven ability to read academic papers and implement their methods in robust Python code
  • Simulation Skills: Experience with differentiable simulation or digital twins (e.g., Sionna, JAX-based physics sims)

Preferred Qualifications
  • Wireless Knowledge: Understanding of wireless fundamentals (OFDM, MIMO, IQ data) is highly helpful, though we prioritize strong ML intuition over pure communication theory
  • Performance Optimization: Experience with model quantization (FP16/INT8), pruning, or using TensorRT for real-time inference
  • Standardization Support: Experience writing technical whitepapers or supporting patent filings in a research environment
  • C++ Integration: Ability to write C++ bindings or integrate Python models into C++, SIMD, and Cuda production pipelines

Working at DeepSig
DeepSig is growing its technical team while cultivating a collaborative, agile, and fun small-team culture. We value creativity, knowledge sharing, and employee growth, and we encourage participation in scientific publications, conferences, and open-source software. We offer competitive salaries and benefits, an employee stock option grant program, an environment where we are excited to be transforming and disrupting how signal processing is done with AI/ML, a welcoming and inclusive environment, a flexible schedule, and a great work / life balance.
DeepSig is an equal-opportunity employer and does not discriminate based on race, ethnicity, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability. We are dedicated to cultivating an inclusive, diverse, and engaging workplace where individuals feel fulfilled, inspired, and motivated. We value the unique perspectives that our team brings.

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