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Computer Vision Deep Learning Jobs (NOW HIRING)

Carbon Robotics

Deep Learning Engineer

Seattle, WA

Lead the design and execution of experiments to develop and validate novel deep learning architectures for computer vision in agricultural environments * Own model optimization and deployment ...

Imagry

Senior Deep Learning Engineer - Perception

San Jose, CA · On-site

$123.70K - $169.90K/yr

Senior Deep Learning Engineer, Computer Vision imagry.E4.E30@comeetapply.com Location: San Jose, CA , On Site We are looking for a capable and experienced Sr. Deep Learning Engineer to join our R&D ...

Your Personal AI

Computer Vision Specialist

Boston, MA · On-site +1

Experience with deep learning frameworks such as TensorFlow or PyTorch * Strong programming skills in languages like Python, C++, or Java * Familiarity with image processing and computer vision ...

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How much do computer vision deep learning jobs pay per year?

As of May 31, 2026, the average yearly pay for computer vision deep learning in the United States is $48,298.00, according to ZipRecruiter salary data. Most workers in this role earn between $38,000.00 and $55,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Computer Vision Deep Learning Engineer, and why are they important?

To thrive as a Computer Vision Deep Learning Engineer, you need a strong background in mathematics, programming (especially Python), and deep learning concepts, often supported by a degree in computer science or a related field. Proficiency with frameworks like TensorFlow, PyTorch, OpenCV, and experience using GPU computing are highly valued, along with relevant certifications in machine learning or artificial intelligence. Strong analytical thinking, creative problem-solving, and effective communication skills set top candidates apart in this role. These competencies are essential for developing, optimizing, and deploying innovative computer vision solutions that address complex real-world challenges.

What are some common challenges faced in a Computer Vision Deep Learning role, and how can they be addressed?

Professionals in Computer Vision Deep Learning often face challenges such as managing large, complex datasets, ensuring high model accuracy, and dealing with real-world variability in images or video. Addressing these issues typically involves data augmentation, careful preprocessing, and the use of advanced architectures like CNNs and transformers. Collaboration with data engineers and domain experts is essential to ensure data quality and to tailor solutions to specific use cases. Additionally, staying updated with the latest research and tools can help in overcoming technical hurdles and enhancing model performance.

What is computer vision deep learning?

Computer vision deep learning is a field of artificial intelligence that leverages deep neural networks to enable computers to interpret and understand visual information from the world, such as images and videos. By using deep learning techniques, such as convolutional neural networks (CNNs), systems can perform tasks like image classification, object detection, and facial recognition with high accuracy. This technology is widely applied in industries including healthcare, automotive, and security for tasks ranging from medical image analysis to autonomous driving.

What is the difference between Computer Vision Deep Learning vs Computer Vision Engineer?

AspectComputer Vision Deep LearningComputer Vision Engineer
Required CredentialsBachelor's or higher in CS, AI, or related fields; knowledge of deep learning frameworksBachelor's or higher in CS or related fields; experience with computer vision algorithms
Work EnvironmentResearch labs, AI startups, tech companies focusing on AI modelsSoftware development teams, product companies, tech firms applying computer vision
Employer & Industry UsageAI research, academia, companies developing deep learning models for vision tasksProduct development, application of computer vision in real-world projects

Computer Vision Deep Learning specialists focus on developing and applying deep learning models for visual data analysis, often involving research and model training. In contrast, Computer Vision Engineers implement and optimize computer vision algorithms within products and applications, emphasizing deployment and practical use. Both roles require a strong foundation in computer vision, but their focus areas and work environments differ.

More about Computer Vision Deep Learning jobs
What job categories do people searching Computer Vision Deep Learning jobs look for? The top searched job categories for Computer Vision Deep Learning jobs are:
Computer Vision Deep Learning jobs near you
Infographic showing various Computer Vision Deep Learning job openings in the United States as of May 2026, with employment types broken down into 2% As Needed, 82% Full Time, 13% Part Time, 1% Temporary, 1% Contract, and 1% Nights. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution, with an average salary of $48,298 per year, or $23.2 per hour.
Cleared Computer Vision Scientist

Cleared Computer Vision Scientist

Accenture Federal Services

Washington, DC

Other

Posted 25 days ago

Accenture Federal Services rating

8.4

Company rating: 8.4 out of 10

Based on 19 frontline employees who took The Breakroom Quiz

47th of 425 rated business services

Job description

The work: 

  • Develop, train, finetune and evaluate computer vision models in a wide range of topics, including geospatial, biometrics, 3D vision, semantic extraction, etc. 
  • Deploy, maintain, and optimize ML models and data processes in a production environment 
  • Develop custom Computer Vision (CV) algorithms that translate into mission value 
  • Create tools to provide feedback from production ML models and data processes 
  • Assist in the development and optimization of computer vision models using deep learning frameworks (e.g., PyTorch, TensorFlow). 
  • Collaborate with other scientists and engineers to build and deploy models for tasks such as object detection, image segmentation, classification, and tracking
  • Stay up to date with the latest research and advancements in the field of computer vision and deep learning
  • Participate in data collection, preprocessing, and augmentation processes to ensure high-quality datasets
  • Conduct experiments, analyze results, and contribute to the improvement of model accuracy and efficiency
  • Assist in the integration of computer vision algorithms into production systems and applications 
  • Document code, methodologies, and experimental results

Here's what you need: 

  • Hands-on experience with computer vision libraries (e.g., OpenCV) and deep learning frameworks (e.g., PyTorch, TensorFlow)
  • Proficiency with programming languages such as Python, C/C++ or Rust 
  • Strong understanding of CNNs, transformers and other advanced architectures and their applications in computer vision tasks
  • Strong analytical and problem-solving skills
  • Ability to work collaboratively in a team environment and take direction from senior team members
  • Hands-on experience with developing computer vision models at scale from inception to business impact 
  • Design and develop custom/novel architectures, define use cases, and develop methodology & benchmarks to evaluate different approaches 
  • U.S. Citizenship (No Dual citizenship)

Bonus points if you have: 

  • Advanced Degree in computer science, technology, engineering, mathematics (STEM) related field, with Ph.D. preferred, but not required
  • Hands-on experience with MLOps and CI/CD toolset including MLFlow, WandB, Airflow, Kubeflow, Gitlab CI or DVC
  • Hands-on experience developing and deploying machine learning pipelines in AWS, Azure or GCP
  • Hands on experience deploying, maintaining, testing, and optimizing ML models and data platforms in a production environment
  • Hands-on experience with other image modalities (SAR, IR, HSI, Lidar, Sonar) 

Security Clearance: 

  • Active Top Secret or TS/SCI or TS/SCI with polygraph Clearance

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