ZipRecruiter

Log In

1

Nvidia Computer Vision Image Processing Jobs (NOW HIRING)

Babel Street

Image & Computer Vision AI Engineer

Reston, VA · On-site

$119K - $143K/yr

This role is execution-focused and suited for engineers with strong foundations in computer vision, image processing, and machine learning who want to apply their skills to real-world, mission-driven ...

Cesna Group

Computer Vision R&D Engineer

San Diego, CA · On-site

$100K - $200K/yr

... image processing, denoising, segmentation and metrology. * - Research, develop and employ machine learning algorithms for solving difficult and exciting challenges. * - Engineer solutions for quality ...

InterImage

Image Processing SME

Fort George G Meade, MD · On-site

Develop and validate machine learning and computer vision models for automated image analysis workflows. * Support integration, testing, verification, and validation of imaging systems and processing ...

next page

Showing results 1-20

All JobsNvidia Computer Vision Image Processing Jobs

Nvidia Computer Vision Image Processing information

See salary details

$5

$62

$89

How much do nvidia computer vision image processing jobs pay per hour?

As of Jun 8, 2026, the average hourly pay for nvidia computer vision image processing in the United States is $62.58, according to ZipRecruiter salary data. Most workers in this role earn between $51.92 and $66.83 per hour, depending on experience, location, and employer.

What are some common challenges faced by professionals working in Nvidia Computer Vision Image Processing roles?

Professionals in Nvidia Computer Vision Image Processing often encounter challenges such as optimizing algorithms for real-time performance, ensuring compatibility across various hardware platforms, and managing large volumes of imaging data efficiently. Additionally, staying current with rapidly evolving deep learning frameworks and GPU architectures is essential to maintain a cutting-edge skill set. Collaboration with cross-functional teams—including data scientists, software engineers, and hardware specialists—is also a key aspect of the role, requiring strong communication and teamwork skills.

What are the key skills and qualifications needed to thrive as an Nvidia Computer Vision Image Processing Engineer, and why are they important?

To thrive as an Nvidia Computer Vision Image Processing Engineer, you need a strong background in computer vision, image processing algorithms, and programming (especially in C++, Python, and CUDA), typically supported by a degree in computer science, electrical engineering, or a related field. Familiarity with deep learning frameworks like TensorFlow or PyTorch, Nvidia's CUDA platform, and tools such as OpenCV is commonly required, along with relevant certifications or project experience. Strong problem-solving, teamwork, and communication skills help individuals stand out, enabling them to collaborate on complex projects and share findings effectively. These competencies are crucial for developing innovative computer vision solutions that leverage Nvidia's hardware and software for high-performance, real-world applications.

What does an Nvidia Computer Vision Image Processing engineer do?

An Nvidia Computer Vision Image Processing engineer develops algorithms and software that enable computers to interpret and analyze visual data, such as images or videos. Their work often involves optimizing deep learning models for tasks like object detection, image segmentation, and pattern recognition, utilizing Nvidia's GPU technologies. These engineers collaborate with research and product teams to implement high-performance solutions for industries such as autonomous vehicles, robotics, healthcare, and entertainment.

What is the difference between Nvidia Computer Vision Image Processing vs Computer Vision Engineer?

AspectNvidia Computer Vision Image ProcessingComputer Vision Engineer
Required CredentialsDegree in Computer Science, Electrical Engineering, or related fields; experience with Nvidia toolsDegree in Computer Science, Electrical Engineering, or related fields; programming skills in Python, C++, and AI frameworks
Work EnvironmentPrimarily in R&D labs, hardware integration, and software development for Nvidia platformsResearch labs, tech companies, or AI startups focusing on developing computer vision applications
Industry UsageUsed within Nvidia for product development, AI hardware, and embedded systemsApplied across various industries like automotive, healthcare, and security for developing vision-based solutions

While Nvidia Computer Vision Image Processing focuses on developing and optimizing vision algorithms using Nvidia hardware and software tools, Computer Vision Engineers design and implement vision systems across industries. Both roles require a strong background in computer vision and programming, but Nvidia specialists are more hardware and platform-oriented, whereas Computer Vision Engineers work on broader application development.

Infographic showing various Nvidia Computer Vision Image Processing job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 89% Full Time, 3% Part Time, and 7% Contract. Highlights an 84% Physical, 8% Hybrid, and 8% Remote job distribution, with an average salary of $130,171 per year, or $62.6 per hour.

Image & Computer Vision AI Engineer

Hatch IT

Reston, VA

Full-time

Posted 15 days ago

Job description

hatch I.T. is partnering with Babel Street to find an Image & Computer Vision AI Engineer. Please see details below:

About the Role

As an Engineer on the Image & Computer Vision AI team, you will play a hands-on role in developing and deploying computer vision capabilities that support Babel Street’s intelligence applications. You will build systems that extract, analyze, and reason over visual data—enabling facial matching, object and scene understanding, geolocation and location inference from imagery, and multimodal intelligence workflows. 

This role is execution-focused and suited for engineers with strong foundations in computer vision, image processing, and machine learning who want to apply their skills to real-world, mission-driven problems. You will work closely with AI, Product, and Engineering teams to deliver reliable, scalable, and cost-efficient vision capabilities, including integration with multimodal LLM systems that allow users to search and reason over images using natural language. 

This is a hybrid role to be based out of either their Reston, VA/Washington DC office or their Somerville MA office.

About the Company

Babel Street is the trusted technology partner for the world’s most advanced identity intelligence and risk operations. They deliver advanced AI and data analytics solutions providing unmatched, analysis-ready data regardless of language, proactive risk identification, 360-degree insights, high-speed automation, and seamless integration into existing systems. Babel Street empowers government and commercial organizations to transform high-stakes identity and risk operations into a strategic advantage.  The actionable insights we deliver safeguard lives and protect critical assets around the world.  Babel Street is headquartered in Reston, Virginia, with regional offices in Boston, MA and Cleveland, OH, and international offices in Australia, Canada, Israel, Japan, and the U.K.

Role Focus:

This role spans three practical execution areas: 

Computer Vision & Image Analytics 

You will implement and operate image analytics pipelines that support facial matching, object detection, scene understanding, and image similarity. This includes image preprocessing, feature extraction, model inference, evaluation, and performance optimization to meet mission-grade accuracy and latency requirements. 

Geospatial & Location Inference from Imagery 

You will contribute to capabilities that infer location, context, or environmental attributes from imagery—leveraging visual cues, metadata, and learned representations. This includes supporting image-based geolocation, landmark recognition, and contextual scene analysis used in intelligence workflows. 

Multi-Modal AI & Image Search 

You will support multimodal AI systems that combine vision models with LLMs, embeddings, and retrieval pipelines to enable natural-language search and reasoning over images and image collections. You will help integrate visual understanding into broader intelligence applications and workflows. 

What you will do:
  • Build and maintain computer vision pipelines for image ingestion, preprocessing, inference, and evaluation. 
  • Implement facial matching, and identity-related vision workflows in accordance with accuracy, safety, and compliance requirements. 
  • Develop and support object detection, image similarity, and scene understanding models. 
  • Contribute to image-based geolocation and location inference capabilities using visual features and contextual signals. 
  • Support multimodal AI workflows that combine image embeddings with LLM-based search and reasoning. 
  • Write clean, maintainable Python code and contribute to production services and APIs. 
  • Assist with model evaluation, bias testing, and accuracy monitoring for vision systems. 
  • Optimize inference pipelines for performance, scalability, and cost efficiency (GPU usage, batching, model selection). 
  • Collaborate with Product and Engineering teams to integrate vision capabilities into user-facing intelligence applications. 
What you will bring:

Required 

  • 3+ years of experience in computer vision, image processing, or applied machine learning. 
  • Hands-on experience with computer vision models and techniques (e.g., CNNs, transformers for vision, feature embeddings). 
  • Experience building or integrating image analytics such as facial recognition, object detection, or image similarity. 
  • Strong programming skills in Python; experience with common CV/ML libraries (PyTorch, TensorFlow, OpenCV, etc.). 
  • Solid understanding of machine learning fundamentals, model evaluation, and performance tradeoffs. 
  • Experience working with large image datasets and production ML pipelines. 
  • Ability to work collaboratively in a fast-moving, mission-driven engineering environment. 

Preferred 

  • Experience with facial matching or biometric systems in regulated or high-stakes environments.
  • Experience with image-based geolocation or scene/location inference.
  • Familiarity with multimodal AI systems, including combining vision models with LLMs or natural-language search. 

Education:

  • Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field required. 
    Advanced degree is a plus but not required. 

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.