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Computer Vision Remote Jobs in Virginia (NOW HIRING)

NT Concepts

Software Engineer, Python

Vienna, VA · On-site +1

... robust computer vision algorithms. You will be part of a solutions-oriented, agile SCRUM team ... Herndon, VA with remote flexibility. Must be local to the DC Metro area. Responsibilities:

SAIC

AI Machine Learning Developer Principal

Chantilly, VA · On-site +1

$160K - $200K/yr

None Potential for Remote Work: ORA_HYBRID Description We are seeking to build a team of AI/ML ... NLP, computer vision, predictive analytics) to address complex IC mission needs. The PoCs can be ...

Parsons

Principal Innovation Engineer

Reston, VA · On-site +1

Experience designing solutions for remote, austere, power-constrained, or intermittently connected ... Experience with AI/ML-enabled analytics , computer vision, anomaly detection, or intelligent ...

SAIC

AI SETA Systems Engineer

Chantilly, VA · On-site +1

$120K - $160K/yr

None Potential for Remote Work: ORA_ON_SITE Description SAIC is seeking a AI/Ml SETA Systems ... computer vision tool portfolio, including developing technical roadmaps and the acquisition ...

SAIC

AI Machine Learning Developer Principal

Arlington, VA · On-site +1

$160K - $200K/yr

None Potential for Remote Work: ORA_HYBRID Description We are seeking to build a team of AI/ML ... NLP, computer vision, predictive analytics) to address complex IC mission needs. The PoCs can be ...

Sony

$50/hr

... computer vision, and audio signal processing. * Strong analytical and programming skills in deep ... Working Location Location flexible (Tokyo, NYC, remote) The target hourly rate for this internship ...

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How much do computer vision remote jobs pay per hour?

As of Sep 11, 2026, the average hourly pay for computer vision remote in Virginia is $20.67, according to ZipRecruiter salary data. Most workers in this role earn between $17.40 and $23.12 per hour, depending on experience, location, and employer.

What is a computer vision remote?

A Computer Vision Remote job involves developing and implementing computer vision algorithms and models while working from a remote location. Professionals in this role work on tasks like image recognition, object detection, and video analysis using AI and machine learning. They collaborate with teams via online communication tools and use cloud platforms or local computing resources for model training. These jobs are common in industries like healthcare, robotics, and autonomous systems.

What are the key skills and qualifications needed to thrive in computer vision remote?

Success as a Computer Vision Remote professional requires expertise in computer vision algorithms, image processing, programming (Python/C++), and a degree in computer science or a related field. Familiarity with popular frameworks such as OpenCV, TensorFlow, or PyTorch, and sometimes certifications in AI or data science, are typically advantageous. Strong communication, self-motivation, and problem-solving skills help remote workers collaborate effectively across distributed teams. These competencies are crucial for delivering high-quality solutions, meeting project deadlines, and excelling in a remote-first work environment.

What are some common challenges faced by remote computer vision professionals, and how are they addressed?

Remote computer vision professionals often encounter challenges such as collaborating across different time zones, accessing large datasets securely, and maintaining consistent communication with onsite teams. These challenges are typically overcome by using version control systems like Git, cloud storage solutions, and regular video meetings to stay connected and ensure alignment. Teams also rely on detailed documentation and code reviews to maintain project quality and foster knowledge sharing. By adopting these best practices, remote professionals can contribute effectively and remain engaged with their team and projects.

What are the most commonly searched types of Computer Vision jobs in Virginia?

The most popular types of Computer Vision jobs in Virginia are:

What are popular job titles related to Computer Vision Remote jobs in Virginia?

For Computer Vision Remote jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Computer Vision Remote jobs in Virginia look for?

The top searched job categories for Computer Vision Remote jobs in Virginia are:

What cities in Virginia are hiring for Computer Vision Remote jobs?

Cities in Virginia with the most Computer Vision Remote job openings:

Computer Vision Remote jobs near you
Infographic showing various Computer Vision Remote job openings in Virginia as of August 2026, with employment types broken down into 1% As Needed, 71% Full Time, 23% Part Time, and 5% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $43,000 per year, or $20.7 per hour.

SAR Data/Imagery Scientist (TS/SCI Clearance required)

Falls Church, VA • On-site, Remote

NorthHill Technology
See more jobs at NorthHill Technologyexternal

Full-time

Re-posted 27 days ago

Key responsibilities

  • Support the evaluation of emerging sensor capabilities and compare new collection platforms with existing systems.

  • Assess the impact of new sensor data on data architectures, including metadata, file formats, schemas, APIs, and ETL processes.

  • Develop preprocessing and standardization workflows to prepare imagery for labeling, analytics, and AI/ML model testing.

Job description

NorthHill Technology Resources has a need for a 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 SAR Data Scientist/Imagery Scientist to join a high-performing team supporting advanced AI and machine learning initiatives for complex national security and intelligence missions.

This program focuses on evaluating AI models against Government datasets to assess their performance, resilience, and robustness across a broad spectrum of adversarial scenarios. The effort also includes testing and validating autonomous algorithms designed to support operational decision-making in dynamic mission environments.

As a SAR Exploitation/Imagery Scientist, you will provide subject matter expertise in Synthetic Aperture Radar (SAR) imagery, geospatial analysis, and quantitative assessment to support data curation, imagery exploitation, and dataset development for machine learning model testing and evaluation. You will collaborate with engineers, data scientists, and mission analysts to ensure imagery products are prepared, standardized, and optimized for AI/ML applications.

Required Qualifications

  • Active TS/SCI clearance with eligibility for CI Polygraph (we can sponsor your CI poly if you don't already have one)
  • 4+ years of experience working with Synthetic Aperture Radar (SAR) imagery, including collection methodologies, radar phenomenology, image formation, and exploitation products.
  • Experience evaluating SAR imagery quality metrics and interpreting sensor metadata, including the effects of collection geometry (e.g., graze angle, squint angle, azimuth) on SAR phenomenology.
  • Demonstrated experience exploiting SAR imagery to detect, identify, and geolocate objects of interest.
  • Strong understanding of remote sensing principles, imagery processing, and advanced SAR exploitation techniques.
  • Excellent communication skills with the ability to effectively present SAR methodologies, imagery products, and analytical findings to both technical and non-technical audiences.
Preferred Qualifications
  • Experience applying computer vision (CV), machine learning (ML), or deep learning techniques to SAR imagery and geospatial data to support intelligence, defense, or remote sensing applications.
Key Responsibilities
  • Support the Lead SAR Scientist in evaluating emerging sensor capabilities and comparing new collection platforms with existing operational systems.
  • Assess the impact of new sensor data on existing data architectures, including metadata, file formats, schemas, APIs, and ETL processes required to ingest and integrate data into operational pipelines.
  • Evaluate data acquisition strategies, expected collection latency, available data formats, and applicable security domains for new sensor sources.
  • Develop preprocessing and standardization workflows to prepare imagery for labeling, analytics, and AI/ML model testing. This may include file format conversion, image tiling, geospatial normalization, and other data preparation activities to ensure compatibility with established data standards.