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Computer Vision Researcher Jobs in California (NOW HIRING)

Pano

Computer Vision Engineer

San Francisco, CA · On-site +1

$141K - $184K/yr

  • Medical

  • Retirement

  • PTO

In this role, you will work alongside experienced AI researchers and engineers to develop, evaluate, optimize, and deploy computer vision models on both cloud and edge devices. You will gain hands-on ...

Meta

Computer Vision Engineer

Burlingame, CA

$183K/yr

This is not a pure research position; we're looking for someone who thrives at turning cutting-edge ML into real products that reach millions of users. Computer Vision Engineer Responsibilities:

Meta

Research Engineer, Computer Vision

Sunnyvale, CA · On-site

$183K - $257K/yr

The ideal candidate will have industry experience working on computer vision and NLP related topics. The position will involve taking these skills and applying them to solve for some of the most ...

Avala AI, Inc

3D Computer Vision Engineer

San Francisco, CA · On-site

$130K - $190K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

The Role As a 3D Computer Vision Engineer at Avala AI, you will design and implement ... Equal Opportunity and Accommodations Research shows that women and BIPOC candidates may hesitate to ...

Apple

Real-Time Computer Vision Architect

Sunnyvale, CA

$184K - $324K/yr

  • Medical

  • Dental

  • Retirement

The Video Computer Vision and Video Engineering teams are centralized applied research and ... We balance research and product development to deliver Apple quality, state-of-the-art experiences.

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Showing results 21-40

All JobsComputer Vision Researcher JobsComputer Vision Researcher Jobs in California

Computer Vision Researcher information

See California salary details

$29.6K

$111.6K

$162.3K

How much do computer vision researcher jobs pay per year?

As of Aug 20, 2026, the average yearly pay for computer vision researcher in California is $111,621.00, according to ZipRecruiter salary data. Most workers in this role earn between $66,100.00 and $152,000.00 per year, depending on experience, location, and employer.

What does a computer vision researcher do?

A Computer Vision Researcher develops algorithms and models that enable computers to interpret and understand visual information from the world, such as images and videos. They work on tasks like object detection, image segmentation, and facial recognition, often using techniques from machine learning and artificial intelligence. Their work is applied in fields such as autonomous vehicles, medical imaging, robotics, and augmented reality. Computer Vision Researchers typically conduct experiments, publish scientific papers, and collaborate with engineers to implement their findings in real-world applications.

What are the key skills and qualifications needed to thrive as a computer vision researcher?

To thrive as a Computer Vision Researcher, you need a strong background in mathematics, machine learning, image processing, and typically a graduate degree in computer science or a related field. Experience with deep learning frameworks (such as TensorFlow or PyTorch), programming languages (like Python or C++), and familiarity with relevant research publications are highly valued. Creativity, problem-solving abilities, and strong communication skills help researchers innovate and effectively share findings. These competencies are vital for advancing technology, developing novel algorithms, and collaborating within multidisciplinary teams.

What are some common challenges faced by computer vision researchers when transitioning from theoretical research to practical applications?

Computer Vision Researchers often encounter challenges when moving from theoretical models to real-world deployment, such as dealing with noisy or incomplete data, ensuring scalability, and optimizing models for real-time performance. Collaborative work with software engineers and domain experts is crucial to address these issues and to adapt state-of-the-art algorithms for production environments. Additionally, staying updated with rapidly evolving tools and frameworks is essential for successfully bridging the gap between research and application.

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

AspectComputer Vision ResearcherMachine Learning Engineer
Required CredentialsMaster's or PhD in Computer Science, AI, or related fieldsBachelor's or Master's in Computer Science, Data Science, or related fields
Work EnvironmentResearch labs, academia, R&D departmentsTech companies, startups, product teams
Employer & Industry UsageUniversities, research institutions, AI-focused companiesTechnology firms, software companies, AI product development
Common Search & ComparisonYesYes

While both roles involve AI and machine learning, a Computer Vision Researcher primarily focuses on developing algorithms for visual data analysis and often works in research settings. In contrast, a Machine Learning Engineer applies machine learning techniques to build scalable AI products and solutions in industry environments.

What are popular job titles related to Computer Vision Researcher jobs in California?

For Computer Vision Researcher jobs in California, the most frequently searched job titles are:

What job categories do people searching Computer Vision Researcher jobs in California look for?

The top searched job categories for Computer Vision Researcher jobs in California are:

Computer Vision Researcher jobs near you
Infographic showing various Computer Vision Researcher job openings in California as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 13% Part Time, and 5% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $111,621 per year, or $53.7 per hour.

Computer Vision Engineer

Pano

San Francisco, CA • On-site, Remote

$141K - $184K/yr

Full-time

Medical, Retirement, PTO

Posted 16 days ago

Job description

Help us tackle the growing wildfire crisis with the latest advancements in AI and IoT
Who we are
The challenge: Every minute matters in wildfire response. As climate change increases the frequency and intensity of wildfires-with longer fire seasons, drier fuels, and more extreme weather-new ignitions can spread rapidly, putting communities, critical infrastructure, and ecosystems at risk. Today, many wildfires are first reported by members of the public, meaning it can take valuable time to detect a fire, confirm its location and size, and mobilize responders. Fire agencies need faster, more reliable ways to detect, verify, and pinpoint new ignitions so they can respond quickly and prevent small fires from becoming catastrophic events.
About Pano AI: Pano AI is the leader in AI-powered wildfire detection and intelligence, helping fire professionals detect, respond to, and contain wildfires faster and more safely. Our platform combines advanced hardware, software, artificial intelligence, satellite imagery, and other data sources to provide real-time situational awareness and actionable intelligence. Using a network of ultra-high-definition, 360-degree cameras positioned across high vantage points, Pano AI delivers a real-time view of wildfire activity, enabling faster, more informed decision-making when every second counts.
We are a team of more than 175 people working in a hybrid-remote environment across North America and Australia, with headquarters in San Francisco. Our customers include government agencies, utilities, insurers, and private landowners who rely on Pano AI to help protect people, property, and natural landscapes. Pano AI currently serves customers across the United States, Australia, and Canada, monitoring more than 50 million acres worldwide.
Our work has been recognized by Fast Company as one of the Top 10 Most Innovative AI Companies in 2023 and one of the World's Most Innovative Companies in 2026, ranking #1 in Sustainability. We have also been named to TIME's list of the 100 Most Influential Companies of 2025 and recognized by MIT Technology Review as one of the top climate technology companies to watch.
Backed by $89 million in funding from leading investors including Giant Ventures, Liberty Mutual Ventures, Tokio Marine Future Fund, Congruent Ventures, Initialized Capital, Salesforce Ventures, and T-Mobile Ventures, we're building technology that helps communities around the world become more resilient to wildfire. Learn more at www.pano.ai.
The Role
We are looking for a motivated Computer Vision Engineer to help build the next generation of cloud/edge-based vision systems for wildfire detection and environmental monitoring.
In this role, you will work alongside experienced AI researchers and engineers to develop, evaluate, optimize, and deploy computer vision models on both cloud and edge devices. You will gain hands-on experience across modern computer vision, edge AI, embedded systems, and real-world AI deployment.
Beyond wildfire detection, you will contribute to a variety of computer vision projects, including vegetation detection, asset recognition, instance segmentation, scene understanding, and spatial reasoning. We value curiosity, adaptability, and a willingness to learn new technologies and tackle diverse technical challenges as our products evolve.
This is an excellent opportunity for an engineer who enjoys learning across the entire AI stack and wants to grow into a senior technical contributor.
What you'll do
  • Assist in developing computer vision models for:
    • Wildfire smoke detection
    • Vegetation detection and classification
    • Asset detection and recognition
    • Instance and semantic segmentation
    • Scene understanding and spatial reasoning
  • Help implement and maintain machine learning and computer vision pipelines.
  • Assist with deploying and optimizing AI models on NVIDIA Jetson and other edge platforms.
  • Support model optimization efforts, including TensorRT conversion, quantization, and inference acceleration.
  • Build tools for data processing, visualization, benchmarking, evaluation, and monitoring.
  • Conduct experiments, analyze model performance, and present findings to the team.
  • Debug inference, deployment, networking, and hardware integration issues.
  • Contribute to continuous learning, model evaluation, and data quality improvement workflows.
  • Collaborate closely with AI researchers, software engineers, hardware engineers, and product teams.
  • Document experiments, engineering decisions, and best practices.
  • Take on a variety of technical challenges as needed and continuously expand your skills across computer vision and cloud/edge AI.

What you'll bring
Required
  • BS or MS in Computer Science, Electrical Engineering, Robotics, or a related field.
  • 1-3 years of experience (including internships or research) in software engineering, machine learning, or computer vision.
  • Experience with Python and deep learning frameworks such as PyTorch.
  • Understanding of machine learning fundamentals and modern computer vision techniques.
  • Familiarity with Linux development environments.
  • Strong problem-solving skills, curiosity, and a desire to learn.
  • Excellent communication and teamwork skills.

Preferred
  • Experience with NVIDIA Jetson, CUDA, TensorRT, ONNX, or embedded AI platforms.
  • Experience with OpenCV.
  • Experience with one or more of the following:
    • Object detection
    • Instance or semantic segmentation
    • Image classification
    • Multi-object tracking
    • Video understanding
  • Familiarity with vision foundation models such as SAM, Grounding DINO, or DINO is a plus.
  • Experience with cloud platforms, MLOps, or CI/CD workflows.
  • Interest in deploying AI systems in real-world environments, particularly outdoor vision systems.

Final compensation for regular full-time employees is determined by a variety of factors, including job-related qualifications, education, experience, skills, knowledge, and geographic location. In addition to base salary, regular full-time roles are eligible for equity. Benefits are tailored to local market standards and statutory requirements in the employee's country of employment, and may include health coverage, retirement or pension contributions, and paid time off. Specific benefit details will be shared during the interview process.

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