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Computer Vision Deep Learning Engineer Jobs in California

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Computer Vision Deep Learning Engineer information

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$47.9K

$119.9K

$135.7K

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

As of Jun 19, 2026, the average yearly pay for computer vision deep learning engineer in California is $119,924.00, according to ZipRecruiter salary data. Most workers in this role earn between $110,000.00 and $129,800.00 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.

What are the most commonly searched types of Computer Vision Deep Learning Engineer jobs in California? The most popular types of Computer Vision Deep Learning Engineer jobs in California are:
What are popular job titles related to Computer Vision Deep Learning Engineer jobs in California? For Computer Vision Deep Learning Engineer jobs in California, the most frequently searched job titles are:
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What cities in California are hiring for Computer Vision Deep Learning Engineer jobs? Cities in California with the most Computer Vision Deep Learning Engineer job openings:
Computer Vision Deep Learning Engineer jobs near you
Infographic showing various Computer Vision Deep Learning Engineer job openings in California as of June 2026, with employment types broken down into 72% Full Time, 21% Part Time, and 7% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $119,924 per year, or $57.7 per hour.
Sr. Computer Vision Engineer (Deep Learning)

Sr. Computer Vision Engineer (Deep Learning)

Harbinger

Mountain View, CA • On-site

$124K - $170K/yr

Full-time

Posted 25 days ago

Job description

Job Summary:
Harbinger is an American commercial electric vehicle (EV) company on a mission to transform an industry starving for innovation. The successful candidate will drive the development and deployment of advanced perception models for Advanced Driver Assistance Systems (ADAS), designing cutting-edge neural network architectures and ensuring reliable deployment on embedded platforms.
Responsibilities:
• Design and implement advanced deep learning architectures to enhance perception capabilities within ADAS systems.
• Maintain and continuously improve existing models by optimizing performance, addressing issues, and refining architecture and algorithms.
• Perform detailed root cause analysis of production issues and develop sustainable, high-quality solutions.
• Optimize model performance with a focus on latency, efficiency, and resource utilization for real-time embedded deployment.
• Integrate and validate deep learning algorithms on automotive-grade hardware and embedded SoCs.
• Collaborate closely with data engineering, data annotation, and platform engineering teams to ensure smooth data flow and seamless model integration.
• Provide regular updates and technical reports on model development, maintenance progress, and performance metrics to management.
Qualifications:
Required:
• 5+ years of professional experience developing, training, validating, and deploying deep learning-based perception models for ADAS or related computer vision applications.
• In-depth understanding of training and inference pipelines, including data loading, augmentation, and loss function design.
• Advanced degree (M.S. or Ph.D.) in Computer Vision, Robotics, Machine Learning, or a closely related discipline, or equivalent industry experience.
• Strong proficiency in Python and a deep understanding of software design principles and development best practices.
• Expertise in PyTorch (preferred) or TensorFlow for large-scale model development and experimentation.
• Practical experience with data pipelines, distributed training, and machine learning experiment management tools.
• Proven ability to work effectively in a collaborative, cross-functional team environment.
Preferred:
• Comprehensive understanding of machine learning algorithms, including classification, regression, and clustering methods.
• Experience deploying and optimizing models for embedded or automotive SoCs (e.g., NVIDIA Drive, TI TDA4, Qualcomm Snapdragon).
• Proficiency in model optimization techniques such as quantization, pruning, and knowledge distillation.
• Doctorate (Ph.D.) in Computer Science, Artificial Intelligence, or related field is a plus.
• Strong programming experience in Python and/or C++ within Linux development environments.
• Familiarity with automotive perception workflows, datasets, and evaluation frameworks (e.g., KITTI, Waymo, Euro NCAP)
Company:
Harbinger is a commercial electric vehicle company that focuses on chassis architecture design. Founded in 2021, the company is headquartered in Garden Grove, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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