ZipRecruiter

Log In

1

Computer Vision Machine Learning Engineer Jobs (NOW HIRING)

Allen Control Systems

WI · On-site

$140 - $180/hr

Senior Computer Vision & Machine Learning Engineer for Autonomous Anti-Drone Systems Company Overview: Allen Control Systems (ACS) is a cutting-edge defense startup founded by two former Navy ...

next page

Showing results 1-20

All JobsTechnology JobsData JobsData Scientist JobsComputer Vision Machine Learning Engineer Jobs

Computer Vision Machine Learning Engineer information

See salary details

$48.5K

$121.5K

$137.5K

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

As of Aug 28, 2026, the average yearly pay for computer vision machine 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 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.

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 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 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.

Are computer vision machine learning engineers in demand?

Computer vision machine learning engineers are in high demand due to the growth of AI applications in industries such as healthcare, automotive, and security. Skills in deep learning frameworks like TensorFlow or PyTorch and experience with image processing are highly valued, and the role often offers competitive salaries and opportunities for advancement.
More about Computer Vision Machine Learning Engineer jobs

What cities are hiring for Computer Vision Machine Learning Engineer jobs?

Cities with the most Computer Vision Machine Learning Engineer job openings:

What states have the most Computer Vision Machine Learning Engineer jobs?

States with the most job openings for Computer Vision Machine Learning Engineer jobs include:

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

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

Computer Vision Machine Learning Engineer jobs near you
Infographic showing various Computer Vision Machine Learning Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 77% Full Time, 16% Part Time, and 6% Contract. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution, with an average salary of $121,515 per year, or $58.4 per hour.

Computer Vision / Machine Learning Engineer

Boston, MA • On-site

AIToolboard
See more jobs at AIToolboardexternal

$82.66 - $123.98/hr

Other

Posted 23 days ago

Job description

Jobs / Computer Vision / Machine Learning Engineer

Computer Vision / Machine Learning Engineer

Contractor

About the Role

Computer Vision / Machine Learning Engineer (Contract)Contract Length: 6 monthsWork Arrangement: Hybrid (onsite 3 days/week in Greater Boston)Computer Vision / Machine Learning Engineer to support an active, production-focused initiative. This role is centered on improving detection and tracking accuracy for object-counting systems operating in real-world, high-throughput environments.The ideal candidate has hands-on experience building and deploying computer vision models, working with multimodal sensor data, and optimizing inference pipelines for edge deployment.

What You’ll Work On
  • Enhancing object detection, segmentation, and tracking accuracy in operational systems
  • Developing and deploying models into validation and pre-production environments
  • Improving real-time performance and reliability on edge hardware
Key Responsibilities
  • Model Development: Train, validate, and deploy computer vision models, with an emphasis on instance segmentation
  • Tracking & Fusion: Implement tracking approaches that combine color and depth data to maintain object persistence across frames
  • Data Quality: Support data curation efforts and audit external annotations to ensure high-quality ground truth
  • Performance Optimization: Tune inference pipelines for low-latency execution on edge platforms
Technical Environment
  • Computer Vision & ML: Instance Segmentation, Object Tracking
  • Sensor Data: RGB + Depth (basic multi-sensor fusion)
  • Edge & Optimization: NVIDIA-based edge hardware, TensorRT or similar acceleration tools
Qualifications
  • Proven experience delivering production-grade ML or CV systems
  • Strong software engineering fundamentals (version control, testing, CI/CD)
  • Experience deploying models beyond experimentation into real environments
  • Ability to meet strict accuracy and performance benchmarks
  • Comfortable working within cloud-only data environments with controlled access policies
Nice to Have
  • Experience optimizing models for edge or embedded systems
  • Familiarity with real-time or near–real-time vision pipelines
  • Background in industrial, robotics, or high-volume operational settings
#J-18808-Ljbffr

Apply