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Computer Vision Machine Learning Jobs in California

Steg.AI

Machine Learning Scientist

Irvine, CA · On-site

Our machine learning team currently consists of 3 PhDs in Computer Vision.We are looking for a highly motivated machine learning scientist with a passion for groundbreaking AI technology for ...

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Computer Vision Machine Learning information

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$12

$19

$29

How much do computer vision machine learning jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for computer vision machine learning in California is $19.66, according to ZipRecruiter salary data. Most workers in this role earn between $16.15 and $21.59 per hour, depending on experience, location, and employer.

What is a computer vision machine learning engineer?

A Computer Vision Machine Learning Engineer is a professional who develops algorithms and models that enable computers to interpret and understand visual data from the world, such as images and videos. They use techniques from machine learning, deep learning, and image processing to build systems capable of tasks like object detection, image classification, facial recognition, and scene understanding. Their work is critical in fields such as autonomous vehicles, healthcare imaging, security, and augmented reality. These engineers typically have strong skills in programming, mathematics, and data analysis, and often work closely with data scientists and software developers.

What are the key skills and qualifications needed to thrive as a computer vision machine learning engineer?

To thrive as a Computer Vision Machine Learning Engineer, you need strong foundations in mathematics, programming (especially Python or C++), and expertise in machine learning algorithms, typically supported by a degree in computer science, engineering, or a related field. Familiarity with deep learning frameworks like TensorFlow or PyTorch and experience with image processing libraries are essential, along with knowledge of version control systems. Strong problem-solving, collaboration, and communication skills help you translate complex requirements into effective models and work efficiently in multidisciplinary teams. These skills ensure the development of robust computer vision solutions that address real-world challenges and drive innovation.

What are some common challenges faced by computer vision machine learning engineers when deploying models to production environments?

Computer Vision Machine Learning engineers often encounter challenges such as ensuring models perform well on real-world, diverse image data that may differ from training datasets. Managing computational efficiency and latency is crucial, especially for real-time applications. Additionally, integrating models with existing software systems and maintaining accuracy as data evolves can be complex. Collaboration with data engineers, software developers, and product teams is essential to address these challenges and ensure smooth deployment and monitoring.

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

AspectComputer Vision Machine LearningData Scientist
Required CredentialsBachelor's or higher in CS, ML, or related; experience with ML frameworksBachelor's or higher in CS, Statistics, or related; strong analytical skills
Work EnvironmentResearch labs, tech companies, AI startups focusing on visual dataBusiness, finance, healthcare sectors analyzing diverse data sets
Industry UsageDeveloping visual recognition systems, image processingData analysis, predictive modeling, business insights
Common Search/ComparisonYesYes

While both roles involve machine learning, Computer Vision Machine Learning specializes in visual data and image processing, whereas Data Scientists work with a broader range of data types to generate insights across various industries.

Is machine learning used in computer vision?

Yes, machine learning is fundamental to computer vision, enabling systems to interpret and analyze visual data such as images and videos. Computer vision professionals often use techniques like deep learning and neural networks to develop applications like object detection, facial recognition, and image classification.

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

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

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

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

What cities in California are hiring for Computer Vision Machine Learning jobs?

Cities in California with the most Computer Vision Machine Learning job openings:

Computer Vision Machine Learning jobs near you
Infographic showing various Computer Vision Machine Learning job openings in California as of September 2026, with employment types broken down into 1% As Needed, 61% Full Time, 33% Part Time, and 5% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $40,886 per year, or $19.7 per hour.

Computer Vision Engineer - 3D

Irvine, CA • On-site

Stark Pharma Solutions Inc
See more jobs at Stark Pharma Solutions Incexternal

$119K - $141K/yr

Contractor

Re-posted 4 days ago

Job description

Hi,

My name is Karthik Mutyala, and I am a Recruitment Manager with Stark Pharma Solutions, specializing in opportunities across the Pharmaceutical, Biotechnology, Medical Device, and Life Sciences industries.

I am actively connecting with professionals for current and upcoming opportunities. If you are open to exploring new roles or would like to stay informed about relevant positions, please send me your updated resume along with the best number and time to reach you.

Role: 3D Data Scientist / Computer Vision Engineer

Location: Irvine, CA (Hybrid)

Duration: 12-Month Contract

Schedule: Monday – Friday | 8:00 AM – 5:00 PM PST

Position Overview

We are seeking a highly skilled 3D Data Scientist / Computer Vision Engineer to support the development and validation of advanced 3D imaging technologies within a cutting-edge digital health and aesthetics environment. This role will focus on validating 3D facial capture systems, developing novel 3D digital biomarkers, and applying machine learning techniques to complex 3D datasets.

The ideal candidate combines expertise in data science, computer vision, machine learning, and 3D modeling technologies, with the ability to translate complex data into clinically meaningful insights.

Key Responsibilities

3D Imaging & Validation

  • Lead validation activities for 3D facial capture and imaging systems.
  • Design and execute validation studies to evaluate accuracy, reproducibility, and performance across diverse datasets.
  • Develop quantitative testing methodologies and statistical frameworks to assess 3D image quality and geometric precision.
  • Document findings and communicate results to technical and business stakeholders.
  • Computer Vision & Digital Endpoint Development
  • Develop and validate novel 3D digital measurements and biomarkers from facial imaging data.
  • Create scalable workflows for processing, analyzing, and extracting features from 3D meshes, point clouds, and photogrammetry data.
  • Support the development of clinically relevant outcome measures using advanced image analysis techniques.
  • Establish best practices for data preprocessing, quality control, and feature engineering.
  • Machine Learning & Data Science
  • Build, train, and evaluate machine learning models using 3D imaging datasets.
  • Apply computer vision, geometric deep learning, and statistical modeling techniques to solve complex analytical challenges.
  • Benchmark model performance and optimize algorithms for robustness, accuracy, and scalability.
  • Collaborate with software, engineering, and scientific teams to deploy and improve analytical solutions.
  • Cross-Functional Collaboration
  • Partner with imaging specialists, software engineers, clinicians, and product teams to define technical requirements and project goals.
  • Support research initiatives and contribute to innovation in digital health, imaging, and computer vision technologies.
  • Stay current with emerging trends in AI, machine learning, computer vision, and 3D data science.

Required Qualifications

  • Bachelor's, Master's, or PhD in Computer Science, Data Science, Computer Vision, Biomedical Engineering, Machine Learning, or a related quantitative field.
  • 3+ years of experience in Data Science, Machine Learning, Computer Vision, or related technical roles.
  • Strong proficiency in Python and data science frameworks such as NumPy, Pandas, SciPy, Scikit-learn, PyTorch, or TensorFlow.
  • Hands-on experience working with:
  • 3D Meshes
  • Point Clouds
  • Depth Maps
  • Photogrammetry Data
  • Experience with 3D visualization, rendering, or modeling tools such as Blender, Autodesk Maya, or similar platforms.
  • Strong background in statistical analysis, validation methodologies, and experimental design.
  • Excellent communication and technical documentation skills.