2

Remote Nvidia Deep Learning Jobs in Virginia (NOW HIRING)

Lead AI Engineer 3624294

Richmond, VA · On-site +1

$180K - $200K/yr

This is a full-time opportunity available to candidates in Richmond, VA on a hybrid basis or remote ... learning, deep learning, transformers, embeddings, vector stores, and LLM-based systems.

... deep learning. Experience with at least two of the following: remote sensing of surface and ground water resources, analysis of satellite gravimetry (GRACE) data, analysis of radar and optical remote ...

Lead AI Engineer

Richmond, VA · On-site +1

$101K - $133K/yr

... or remote applicants residing in states/locations under Eastern Standard Time: Connecticut ... Evaluate when to apply classical ML, deep learning, or LLM-driven approaches based on business ...

Lead AI Engineer

Richmond, VA · On-site +1

$101K - $133K/yr

... or remote applicants residing in states/locations under Eastern Standard Time: Connecticut ... Evaluate when to apply classical ML, deep learning, or LLM-driven approaches based on business ...

Senior Consultant Data Scientist Arlington, Virginia, United States, Remote Flexible Excella is a ... Extensive experience applying machine learning techniques, including deep learning and NLP. 

next page

Showing results 1-20

Remote Nvidia Deep Learning information

What is the difference between Remote Nvidia Deep Learning vs Remote Machine Learning Engineer?

AspectRemote Nvidia Deep LearningRemote Machine Learning Engineer
Required CredentialsDeep learning certifications, Nvidia GPU expertise, programming skills in Python and CUDAMachine learning certifications, Python, data analysis, model deployment skills
Work EnvironmentRemote, GPU-intensive tasks, AI research, model trainingRemote, data processing, model development, deployment
Industry UsageAI research labs, tech companies, autonomous vehiclesTech firms, finance, healthcare, e-commerce

Remote Nvidia Deep Learning focuses on developing AI models using Nvidia GPUs and CUDA, often in research or AI-specific roles. Remote Machine Learning Engineers work on building and deploying machine learning models across various industries. While both roles require programming and data skills, Nvidia Deep Learning emphasizes GPU expertise and AI research, whereas Machine Learning Engineers focus on broader model deployment and application.

What job categories do people searching Remote Nvidia Deep Learning jobs in Virginia look for? The top searched job categories for Remote Nvidia Deep Learning jobs in Virginia are:
What cities in Virginia are hiring for Remote Nvidia Deep Learning jobs? Cities in Virginia with the most Remote Nvidia Deep Learning job openings:
Infographic showing various Remote Nvidia Deep Learning job openings in Virginia as of July 2026, with employment types broken down into 7% Internship, 79% Full Time, 7% Part Time, and 7% Contract. Highlights an 7% In-person, and 93% Remote job distribution.

LEAD SAR DATA/IMAGERY SCIENTIST (TS/SCI Clearance required)

NorthHill Technology

Falls Church, VA • On-site, Remote

Full-time

Posted 7 days ago


Job description

NorthHill Technology Resources has a need for a Lead SAR Data Scientist/Imagery Scientist to join a Federal Program in Falls Church, VA.  This is a direct-hire opportunity with our client, a highly respected Federal Integrator.  A current TS/SCI Clearance is required. 

We are seeking an experienced Lead SAR Data Scientist / Imagery Scientist to join a high-performing team developing advanced solutions for complex national security and intelligence challenges.

This program focuses on evaluating artificial intelligence and machine learning models against existing government datasets to assess robustness across a wide range of adversarial scenarios. The team will also support the development and evaluation of automated algorithms capable of making operational decisions in dynamic environments.

As the Lead SAR Exploitation / Imagery Scientist, you will serve as the subject matter expert (SME) for Synthetic Aperture Radar (SAR) imagery. You will provide technical leadership in the acquisition, preparation, processing, and exploitation of SAR data, ensuring imagery meets Government quality standards and mission requirements. You will also develop solutions that account for the unique phenomenology, capabilities, and limitations of SAR sensors and collection platforms.


Required Qualifications
  • Active TS/SCI clearance with eligibility for CI Polygraph (we can sponsor your CI poly if you don't already have one)
  • 6+ years of experience as a SAR exploitation or imagery expert with a strong understanding of:
    • SAR collection principles
    • Radar phenomenology
    • Image formation processes
    • SAR exploitation products
  • Experience using SARPy and the MATLAB SAR Toolbox
  • Knowledge of SAR image quality assessment, including:
    • RNIIRS
    • Information-theoretic image quality metrics
    • Integrated Sidelobe Ratio (ISLR)
    • Multiplicative Noise Ratio (MNR)
  • Experience interpreting sensor metadata and understanding how collection geometry (e.g., graze angle, squint angle, azimuth) impacts SAR phenomenology
  • Demonstrated experience exploiting SAR imagery to detect, identify, and geolocate objects of interest
  • Experience developing, testing, and evaluating SAR processing algorithms, methodologies, and analytical products
  • Proficiency with advanced data processing and scientific computing tools such as PythonMATLAB, and Google Earth Engine
  • Strong understanding of remote sensing principles, imagery processing, and advanced exploitation techniques
  • Excellent written and verbal communication skills with the ability to present technical concepts to both technical and non-technical stakeholders

Preferred Qualifications
  • Experience applying computer vision (CV), machine learning (ML), or deep learning techniques to SAR imagery for intelligence, defense, or remote sensing applications
  • Experience supporting AI/ML model development, validation, or operational testing using SAR datasets

Key Responsibilities
  • Evaluate emerging SAR sensor capabilities and compare performance against currently fielded collection platforms.
  • Assess the impact of new sensor data on existing data architectures, including metadata, file formats, schemas, APIs, and ETL processes required for integration into operational data pipelines.
  • Determine data acquisition strategies, expected collection latency, available data formats, and applicable security domains for new sensor sources.
  • Design and implement preprocessing workflows to standardize imagery for labeling, analysis, and AI/ML model evaluation, including format conversion, image tiling, and geospatial normalization.
  • Identify capability gaps associated with emerging sensors and recommend complementary data sources when appropriate.
  • Assess opportunities for multi-source and coincident collections by identifying complementary imagery from additional platforms (e.g., EO/IR) with comparable geographic and temporal coverage.
  • Provide technical leadership on SAR phenomenology, exploitation methodologies, and sensor performance to support Government mission objectives.
  • Collaborate with data scientists, AI/ML engineers, and mission analysts to ensure SAR data is optimized for advanced analytics and model development.