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Computer Vision Machine Learning Engineer Jobs in California

Winaxis

Machine Learning Engineer

Fremont, CA · On-site

Our direct client is hiring a Machine Learning Engineer for their software machine learning and computer vision team to design, develop, and implement critical machine learning models supporting ...

MM International

Machine Learning Engineer

Fremont, CA · On-site

Our direct client is hiring a Machine Learning Engineer for their software machine learning and computer vision team to design, develop, and implement critical machine learning models supporting ...

Hadrian

Machine Learning Engineer

Los Angeles, CA · On-site

... in Computer Vision and/or Machine Learning, including ownership of projects throughout the entire ML Lifecycle • Proficiency in Python, OpenCV, SQL, and one or more deep learning frameworks ...

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Computer Vision Machine Learning Engineer information

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$47.9K

$119.9K

$135.7K

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

As of May 30, 2026, the average yearly pay for computer vision machine 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 are the key skills and qualifications needed to thrive as a Computer Vision Machine Learning Engineer, and why are they important?

To thrive as a Computer Vision Machine Learning Engineer, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant degree and experience with image processing algorithms. Proficiency with frameworks like TensorFlow or PyTorch, knowledge of OpenCV, and experience with cloud computing platforms are commonly required. Problem-solving ability, teamwork, and effective communication are crucial soft skills for integrating complex models into real-world applications. These skills and qualities are essential for developing innovative solutions that accurately interpret visual data and drive impactful results.

What are some common challenges Computer Vision Machine Learning Engineers face when deploying models to production environments?

One common challenge for Computer Vision Machine Learning Engineers is ensuring that models perform reliably in real-world conditions, which can vary significantly from controlled training datasets. Handling data drift, optimizing inference speed for deployment on edge devices, and integrating models into existing software pipelines all require close collaboration with software engineers, data scientists, and product teams. Additionally, managing hardware resource constraints and maintaining model accuracy as new data is collected are ongoing responsibilities. Staying up to date with the latest research and tools is essential to address these evolving challenges effectively.

What does a Computer Vision Machine Learning Engineer do?

A Computer Vision Machine Learning Engineer designs and develops algorithms that enable computers to interpret and understand visual data from the world, such as images and videos. They use machine learning techniques to train models for tasks like object detection, facial recognition, and image segmentation. These engineers typically work with large datasets, optimize models for accuracy and efficiency, and deploy solutions for real-world applications in industries like healthcare, automotive, robotics, and retail.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) working in computer vision develop and deploy AI models, but AI is a tool that enhances their work rather than replacing the role entirely. MLEs are needed to design, optimize, and maintain complex systems, especially when integrating new algorithms or handling data preprocessing. AI automation can assist with tasks, but human expertise remains essential for innovation and troubleshooting in the field.

What is the difference between Computer Vision Machine Learning Engineer vs Data Scientist?

AspectComputer Vision Machine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, ML, or related; experience with CV frameworksBachelor's or Master's in CS, Statistics, or related; strong analytical skills
Work EnvironmentDevelops CV models, works with image/video data, often in AI/tech companiesAnalyzes data, builds predictive models, often in finance, healthcare, or tech
Industry UsageCommon in AI, robotics, autonomous vehicles, surveillanceUsed across finance, marketing, healthcare, and tech sectors

While both roles involve machine learning, Computer Vision Machine Learning Engineers focus on developing models for image and video data, often requiring specialized knowledge in CV frameworks. Data Scientists analyze diverse datasets to extract insights, with less emphasis on visual data. Both roles share foundational ML skills but differ in their application domains.

What are popular job titles related to Computer Vision Machine Learning Engineer jobs in California? For Computer Vision Machine Learning Engineer jobs in California, the most frequently searched job titles are:
What job categories do people searching Computer Vision Machine Learning Engineer jobs in California look for? The top searched job categories for Computer Vision Machine Learning Engineer jobs in California are:
What cities in California are hiring for Computer Vision Machine Learning Engineer jobs? Cities in California with the most Computer Vision Machine Learning Engineer job openings:
Computer Vision Machine Learning Engineer jobs near you
Machine Learning Engineer - Computer Vision & Data Systems

Machine Learning Engineer - Computer Vision & Data Systems

Apple

Sunnyvale, CA • On-site

$133.60K - $160.50K/yr

Full-time

Posted 12 days ago

Apple rating

8.1

Company rating: 8.1 out of 10

Based on 661 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers

Job description

Job Summary:
Apple is dedicated to creating technologies that enrich people's lives, and they are seeking a Machine Learning Engineer with expertise in computer vision and large-scale data processing. The role involves designing algorithms, building scalable data pipelines, and collaborating with multi-functional teams to deliver high-impact, production-quality solutions.
Responsibilities:
• Design, build, and maintain large-scale data processing workflows, ensuring efficiency, scalability, and reliability across diverse data sources and modalities.
• Develop and optimize computer vision models that power core product experiences, including areas such as image understanding, multi-view geometry, 3D reconstruction, and visual recognition.
• Partner closely with engineering, research, and data teams to translate product requirements into technical solutions. This includes prototyping models, running large-scale experiments, improving data quality, and ensuring seamless integration of algorithms into production systems.
• Explore emerging areas such as LLM-based agents, retrieval-augmented systems, and tool-oriented reasoning to improve internal workflows or data operations.
Qualifications:
Required:
• Strong foundation in computer vision, including experience with deep learning-based vision models and at least one area such as detection, segmentation, 3D vision, geometric methods, tracking, or self-supervised learning.
• Hands-on experience developing machine learning models using frameworks such as PyTorch or TensorFlow.
• Experience building or optimizing large-scale data pipelines (e.g., distributed ETL, dataset generation, annotation workflows, data validation, or high-throughput processing).
• Proficiency in Python or C++ for algorithm development and data processing.
• Experience working with distributed computing frameworks (e.g., Spark, Ray, or equivalent).
Preferred:
• PhD in a relevant field with research directly related to computer vision, large-scale data systems, or multimodal learning.
• Experience designing or evaluating agentic systems, including LLM-powered tools, RAG pipelines, or automated data reasoning workflows.
• Familiarity with prompt engineering, tool-use patterns, and LLM model behavior.
• Experience deploying ML models at scale, including monitoring, evaluation, and continuous improvement.
• Knowledge of data quality assessment, dataset curation methodologies, and evaluation frameworks.
• Experience with GPU-based optimization, large-batch training, or distributed training.
• Strong multi-functional collaboration skills and the ability to lead technical initiatives.
Company:
Apple is a technology company that designs, manufactures, and markets consumer electronics, personal computers, and software. Founded in 1976, the company is headquartered in Cupertino, USA, with a team of 10001+ employees. The company is currently Late Stage.

What Apple employees say

Pay

Benefits

Hours and flexibility

Workplace

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

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