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Computer Vision Deep Learning Jobs (NOW HIRING)

About Matroid Matroid is a full-service computer vision company that has developed an end-to-end ... We are looking for a world-class Deep Learning Software Engineer who is excited to operate at the ...

Deep Learning Engineer

Palo Alto, CA · On-site

$170K - $300K/yr

About Matroid Matroid is a full-service computer vision company that has developed an end-to-end ... We are looking for a world-class Deep Learning Software Engineer who is excited to operate at the ...

About Matroid Matroid is a full-service computer vision company that has developed an end-to-end ... We are looking for a world-class Deep Learning Software Engineer who is excited to operate at the ...

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Computer Vision Deep Learning information

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$33.5K

$48.3K

$63.5K

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

As of Aug 27, 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 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 are the key skills and qualifications needed to thrive as a computer vision deep learning engineer?

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 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.

Is deep learning used in computer vision?

Yes, deep learning is widely used in computer vision, especially in roles like Computer Vision Deep Learning specialists. It enables tasks such as image recognition, object detection, and segmentation by training neural networks on large datasets using frameworks like TensorFlow or PyTorch.
More about Computer Vision Deep Learning jobs
Infographic showing various Computer Vision Deep Learning job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 15% Part Time, and 6% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $48,298 per year, or $23.2 per hour.

Computer Vision / Deep Learning Scientist (Atlanta)

Aptonet

Atlanta, GA • On-site

Full-time

Posted 9 days ago


Job description

Computer Vision / Deep Learning Scientist – SeniorPosition Overview

We are seeking a Senior Computer Vision / Deep Learning Scientist to design, develop, and deploy advanced computer vision and deep learning solutions for unique, high-impact applications. The successful candidate will combine strong theoretical knowledge with hands‑on industry experience to develop novel algorithms, custom deep learning architectures, and production‑ready machine learning solutions. This role will be a key contributor to a Digital Train Inspection project, applying computer vision and deep learning techniques to analyze visual data and support automated inspection capabilities.

The Senior Scientist will own the model development lifecycle from requirements gathering and data evaluation through model development, validation, production integration, and post‑production support. The role requires close collaboration with application development teams, business stakeholders, and senior leadership, as well as the ability to provide technical guidance to junior team members and lead targeted research initiatives.

Work Arrangement
  • Hybrid schedule: Two days onsite each week for candidates located in the Atlanta area.
  • Fully remote option available for candidates located outside the Atlanta area.
Key Responsibilities
  • Design, develop, and implement novel computer vision algorithms for specialized and unique use cases.
  • Design and build custom deep learning architectures tailored to specific business and technical requirements.
  • Develop and apply deep learning models for semantic segmentation, object detection, image classification, and related computer vision applications.
  • Evaluate model accuracy, robustness, quality, and performance, as well as the quality and suitability of underlying data sources.
  • Develop clean, scalable, maintainable, and production‑ready Python and machine learning code.
  • Partner with application development teams to integrate computer vision and deep learning models into existing applications and production environments.
  • Own the complete model development lifecycle, including requirements gathering, data assessment, experimentation, model development, validation, deployment, monitoring, and post‑production support.
  • Conduct research and experimentation to identify and implement new computer vision and deep learning techniques.
  • Communicate technical findings, model performance, research results, and recommendations clearly to colleagues, business partners, and senior management.
  • Provide technical guidance and mentorship to junior team members and oversee targeted research and team projects.
  • Contribute to hiring initiatives, including technical candidate evaluation, interviews, and assessment of prospective team members.
  • Collaborate across technical and business functions to translate complex computer vision challenges into practical, scalable solutions.
Required Qualifications
  • Bachelor’s, Master’s, or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, Statistics, or a related technical field.
  • 1–3 years of relevant industry experience in a role such as Computer Vision Scientist, Data Scientist, Research Scientist, Machine Learning Scientist, or similar; 3+ years of experience is preferred. Candidates with equivalent proven qualifications may also be considered.
  • Excellent programming skills in Python, with demonstrated experience developing machine learning or deep learning solutions in an industry environment.
  • Hands‑on industry experience with PyTorch or another major deep learning framework.
  • Strong practical experience applying deep learning techniques to computer vision problems, including semantic segmentation, object detection, and image classification.
  • Ability to evaluate model performance and data quality and translate findings into actionable improvements.
  • Experience developing production‑ready machine learning solutions and collaborating with software/application development teams.
  • Strong analytical, problem‑solving, communication, and research skills.
Preferred Qualifications
  • 3+ years of professional experience in computer vision, machine learning, deep learning, or a closely related discipline.
  • Experience taking machine learning models from research or experimentation into production.
  • Experience working with large‑scale visual datasets and establishing data quality and model evaluation processes.
  • Experience mentoring technical professionals or leading research‑oriented projects.
  • Experience participating in technical recruiting, interviewing, or candidate evaluation.
Project Focus: Digital Train Inspection

The selected candidate will contribute to a Digital Train Inspection initiative, developing computer vision and deep learning capabilities that support automated analysis of train and rail‑related visual inspection data. The work will involve applying advanced image analysis and machine learning techniques to identify, classify, segment, and evaluate visual conditions relevant to inspection and maintenance workflows.

Core Technical Skills
  • Python
  • PyTorch or comparable deep learning frameworks
  • Computer Vision
  • Semantic Segmentation
  • Image Classification
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