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Computer Vision Deep Learning Engineer Jobs (NOW HIRING)

Carbon Robotics

Deep Learning Engineer

Seattle, WA · On-site

As a Deep Learning Engineer, you will design, develop, and deploy deep learning systems for ... for computer vision in agricultural environments • Own model optimization and deployment ...

NBCUniversal

Deep Learning Engineer

Manhattan, NY · On-site

They are seeking a Deep Learning Engineer to implement algorithms that integrate computer vision and graphics, focusing on transforming large datasets into high-fidelity content. Responsibilities ...

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

As of Jul 9, 2026, the average yearly pay for computer vision deep learning engineer in the United States is $121,515.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,500.00 and $131,500.00 per year, depending on experience, location, and employer.

What is a Computer Vision Deep Learning Engineer job?

A Computer Vision Deep Learning Engineer designs, develops, and optimizes deep learning models for tasks like image recognition, object detection, and segmentation. They work with large datasets, train neural networks, and fine-tune models to achieve high accuracy. The role involves using frameworks like TensorFlow or PyTorch, implementing computer vision algorithms, and deploying models for real-world applications. Strong programming skills in Python and experience with deep learning techniques are essential.

What are some typical challenges faced by Computer Vision Deep Learning Engineers in their daily work?

Computer Vision Deep Learning Engineers often face challenges such as handling large, complex datasets, tuning deep learning models to achieve high accuracy, and optimizing processing speed for real-time applications. They may encounter difficulties with noisy or incomplete data, require advanced troubleshooting when models underperform, and must stay updated with rapid advancements in the field. Collaboration with data scientists, software engineers, and product teams is common, so balancing technical depth with effective communication is key. Overcoming these challenges requires strong analytical skills, continuous learning, and a proactive problem-solving mindset.

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

To thrive as a Computer Vision Deep Learning Engineer, you need a strong background in machine learning, computer vision, mathematics, and programming (usually Python or C++), often supported by a relevant degree such as computer science, electrical engineering, or a related field. Experience with frameworks like TensorFlow, PyTorch, OpenCV, and familiarity with cloud computing environments or GPU acceleration are typically essential, with additional value from certifications in AI or deep learning. Strong problem-solving abilities, teamwork, and clear communication are valuable soft skills for collaborating on complex research and product development projects. These skills and qualifications ensure effective design, implementation, and integration of state-of-the-art computer vision solutions within multidisciplinary teams and real-world applications.

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Computer Vision Deep Learning Engineer jobs near you
Deep Learning Engineer

Deep Learning Engineer

Carbon Robotics

Seattle, WA • On-site

Full-time

Re-posted 22 days ago

Job description

Job Summary:
Carbon Robotics is an innovative company focused on revolutionizing agriculture through advanced robotics and AI technology. As a Deep Learning Engineer, you will design, develop, and deploy deep learning systems for autonomous laser weeding robots, ensuring high performance and scalability in agricultural environments.
Responsibilities:
• 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 pipelines — ensuring high performance, reliability, and scalability across operational field deployments
• Drive end-to-end ML workflows from data strategy and pipeline design through evaluation and production deployment
• Define best practices for experimentation, documentation, and model evaluation within the team
• Partner with Engineering and Product Management to scope, prioritize, and deliver high-impact features
• Mentor and provide technical guidance to mid-level and junior engineers
• Communicate model architecture decisions, tradeoffs, and performance results to both technical and non-technical audiences
Qualifications:
Required:
• 2-4 years of professional experience designing and implementing novel deep learning architectures for production computer vision systems
• Deep understanding of foundational deep learning mathematics and the ability to apply first-principles thinking to architecture decisions
• Hands-on experience working across the software stack, including sensor integration and web services, ideally within a robotics or autonomous field equipment platform
• Experience with deep learning frameworks, particularly PyTorch, and proficiency in C++ for performance-critical model development and deployment
• Proven track record taking ML projects from inception through business impact — including data strategy, pipeline development, experimentation, and deployment at scale
• Strong expertise in modern object detection techniques (vision transformers, anchor-free detectors, embeddings, and beyond)
• Experience in autonomous driving or ADAS is a plus — background in perception pipelines, sensor fusion, or real-time inference in outdoor or unstructured environments is highly valued
• Comfort navigating ambiguity and making principled technical decisions in rapidly evolving technical landscapes
• Strong verbal and written communication skills — able to explain complex model behavior and tradeoffs to non-technical staff and customers
• Experience mentoring engineers and contributing to team technical culture
• 2-7 years of experience in deep learning model optimization and deployment
• BS+ in Computer Science, Machine Learning, or a related field (or equivalent experience)
• We're a collaborative, in-person team — this role is based in our Seattle office with at least 4 days per week on-site
Preferred:
• Experience in autonomous driving or ADAS is a plus — background in perception pipelines, sensor fusion, or real-time inference in outdoor or unstructured environments is highly valued
Company:
Carbon Robotics is revolutionizing agriculture with AI and robotics to reduce costs and increase yields Founded in 2018, the company is headquartered in Seattle, USA, with a team of 51-200 employees. The company is currently Growth Stage.

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