Computer Vision Engineer Jobs
Computer Vision Engineer jobs are open across automotive, robotics, healthcare, defense, and consumer tech, from new-grad to staff and principal levels, with specializations in object detection, 3D reconstruction, and autonomous systems. Find a role that fits from the openings below and apply directly.
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The incredible potential of multimodal foundation models and large language models has unlocked machine learning applications that were previously thought infeasible. The Video Computer Vision (VCV) group is looking for a highly motivated and skilled Machine Learning Systems Engineer to help us ship cutting-edge computer vision technology on Apple devices.
The VCV organization has pioneered groundbreaking features like FaceID/FaceKit, Gaze/Hand Gesture Control, Body Tracking, and 2D/3D Scene Understanding fundamentally changing how millions of users interact with technology. We seamlessly balance research and product requirements to deliver pioneering, Apple-quality experiences. By innovating across the full stack and partnering closely with hardware, software, and AI teams, we shape future products and bring our architectural vision to life.
Description
As a member of the Video Computer Vision team, you will work across the stack to transform promising research projects into refined products that ship to our customers. You will build software infrastructure to deliver multimodal context to models and optimize the software harness and the model for efficient on-device deployment. Partnering closely with algorithm researchers, you will also participate in post-training model alignment and evaluation.","responsibilities":"Develop on-device software that bridges multimodal AI models and computer vision technologies with production systems deployed across Apple devices.
Optimize on-device inference latency, memory footprint, and computational efficiency of CV/ML models.
Benchmark, profile, and evaluate the power consumption and thermal performance of models running on Apple silicon.
Engage with cross-functional teams to understand and influence requirements, brainstorm the solution space, and propose, design and implement debuggable and maintainable Computer Vision systems meeting requirements.
Design experiments and evaluations to understand quality and on-device deployment tradeoffs, driving informed system design decisions.
Debug concurrent systems on embedded devices via a variety of existing tools and techniques; develop innovative debug tools and visualizations when existing ones don't fit the bill.
Preferred Qualifications
Programming experience with Swift and familiarity with CoreML, CoreFoundation, and RealityKit frameworks.
Fundamental knowledge of real-time video pipelines, image transformations, and rendering loops.
Experience optimizing models for neural network accelerators (e.g., Apple Neural Engine or mobile GPUs).
Minimum Qualifications
Bachelor’s degree in Computer Science, Machine Learning, or a related discipline, and 3+ years of relevant industry experience.
Strong ML fundamentals.
Proven track record of writing high-quality production code for shipped on-device CV/ML features deployed on embedded platforms
Solid understanding of operating system fundamentals and extensive programming experience in Python and C++.
Hands-on experience with PyTorch and familiarity with the end-to-end ML lifecycle (data preprocessing, training, evaluation, and edge deployment).
Experience with Supervised Fine-Tuning (SFT) pipelines to adapt vision and multimodal foundation models for specialized, on-device downstream tasks.
Robust foundational understanding of machine learning architectures, specifically Multimodal LLMs and the integration of ML components into complex production systems.
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
The incredible potential of multimodal foundation models and large language models has unlocked machine learning applications that were previously thought infeasible. The Video Computer Vision (VCV) group is looking for a highly motivated and skilled Machine Learning Systems Engineer to help us ship cutting-edge computer vision technology on Apple devices.
The VCV organization has pioneered groundbreaking features like FaceID/FaceKit, Gaze/Hand Gesture Control, Body Tracking, and 2D/3D Scene Understanding fundamentally changing how millions of users interact with technology. We seamlessly balance research and product requirements to deliver pioneering, Apple-quality experiences. By innovating across the full stack and partnering closely with hardware, software, and AI teams, we shape future products and bring our architectural vision to life.
Description
As a member of the Video Computer Vision team, you will work across the stack to transform promising research projects into refined products that ship to our customers. You will build software infrastructure to deliver multimodal context to models and optimize the software harness and the model for efficient on-device deployment. Partnering closely with algorithm researchers, you will also participate in post-training model alignment and evaluation.","responsibilities":"Develop on-device software that bridges multimodal AI models and computer vision technologies with production systems deployed across Apple devices.
Optimize on-device inference latency, memory footprint, and computational efficiency of CV/ML models.
Benchmark, profile, and evaluate the power consumption and thermal performance of models running on Apple silicon.
Engage with cross-functional teams to understand and influence requirements, brainstorm the solution space, and propose, design and implement debuggable and maintainable Computer Vision systems meeting requirements.
Design experiments and evaluations to understand quality and on-device deployment tradeoffs, driving informed system design decisions.
Debug concurrent systems on embedded devices via a variety of existing tools and techniques; develop innovative debug tools and visualizations when existing ones don't fit the bill.
Preferred Qualifications
Programming experience with Swift and familiarity with CoreML, CoreFoundation, and RealityKit frameworks.
Fundamental knowledge of real-time video pipelines, image transformations, and rendering loops.
Experience optimizing models for neural network accelerators (e.g., Apple Neural Engine or mobile GPUs).
Minimum Qualifications
Bachelor’s degree in Computer Science, Machine Learning, or a related discipline, and 3+ years of relevant industry experience.
Strong ML fundamentals.
Proven track record of writing high-quality production code for shipped on-device CV/ML features deployed on embedded platforms
Solid understanding of operating system fundamentals and extensive programming experience in Python and C++.
Hands-on experience with PyTorch and familiarity with the end-to-end ML lifecycle (data preprocessing, training, evaluation, and edge deployment).
Experience with Supervised Fine-Tuning (SFT) pipelines to adapt vision and multimodal foundation models for specialized, on-device downstream tasks.
Robust foundational understanding of machine learning architectures, specifically Multimodal LLMs and the integration of ML components into complex production systems.
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $150,400 and $277,600, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
Computer Vision Engineer Jobs by Experience Level
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Find Computer Vision Engineer JobsComputer Vision Engineer Job Market
Who's Hiring
Top Industries Hiring
- Technology & Software
- Electronics & Hardware
- Telecommunications
- Consulting & Professional Services
- Manufacturing
What Employers Look For
The qualifications that appear most often in computer vision engineer jobs.
- Proficiency in Python with deep learning frameworks such as PyTorch or TensorFlow
- Hands-on experience with OpenCV and classical image processing techniques
- Strong understanding of convolutional neural network architectures and object detection models
- Experience deploying models to production environments including cloud or edge hardware
- Familiarity with 3D vision techniques such as point cloud processing or stereo depth estimation
- Bachelor's or master's degree in computer science, electrical engineering, or a related field
Tips for Your Computer Vision Engineer Job Search
Quantify your model performance results
Hiring managers want numbers, not descriptions. Replace 'improved detection accuracy' with the exact mAP score, inference latency reduction, or false-positive rate you achieved. Reviewers skim resumes fast, and concrete metrics on real datasets make your work immediately credible.
Tailor your GitHub to each application
Pin repositories that match the stack in the job description. If a role emphasizes real-time edge inference, push your TensorRT or ONNX runtime project to the top. Reviewers often check GitHub before the phone screen, so alignment there can determine whether you advance.
Apply early to roles that fit
Migrate Mate lists computer vision engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target job descriptions by deployment environment
Cloud-hosted pipeline roles and embedded edge-device roles call for different skills. Read postings carefully for keywords like CUDA, OpenVINO, or FPGA to distinguish them. Applying to roles that match your actual deployment experience dramatically improves your callback rate.
Prepare a live demo for technical screens
Many computer vision interview loops include a take-home or live coding task involving image processing or model inference. Practice explaining your architectural choices out loud, not just writing code. Interviewers weigh your reasoning as heavily as your solution.
Negotiate using competing offer timelines
If you have multiple interviews in flight, coordinate your offer deadlines before any explode. Letting a hiring team know you have another process moving forward is standard and often accelerates their decision. Use that window to request additional time or a competing offer match.
Computer Vision Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most computer vision engineers?
The companies hiring the most computer vision engineers right now include Meta, Qualcomm, and Booz Allen Hamilton, with the largest share of openings in California, Virginia, and New York, based on current listings on Migrate Mate as of November 2026. Automotive, defense, and consumer robotics employers tend to post the highest volumes of these roles.
How many computer vision engineer jobs are remote?
About 54% of computer vision engineer openings are fully remote or hybrid as of November 2026, making it a more location-flexible field than many hardware-adjacent engineering disciplines. Research-focused and cloud-pipeline roles tend to be the most remote-friendly, while positions involving physical cameras, robots, or embedded systems typically require on-site presence.
How do you become a computer vision engineer?
Start by building a strong foundation in linear algebra, calculus, and probability, then learn Python alongside a deep learning framework such as PyTorch. Work through publicly available datasets like COCO or ImageNet to build hands-on experience with detection and segmentation pipelines. Complete projects you can publish to GitHub, contribute to open-source vision libraries, and apply to entry-level or internship roles that involve real deployment work.
Can you get hired as a computer vision engineer without much experience?
Yes, but your portfolio has to substitute for work history. Build end-to-end projects that go beyond notebook tutorials, such as a deployable object detection app or a real-time inference pipeline running on your own hardware. Open-source contributions, Kaggle competition placements in vision tracks, and published research, even preprints, all signal practical ability to hiring teams evaluating candidates without a professional resume.
What does the computer vision engineer interview process look like?
Most processes include an initial recruiter call, a technical phone screen covering Python and deep learning fundamentals, and a multi-stage loop with a take-home or live coding task focused on image processing or model evaluation. Later rounds typically involve a system design discussion around vision pipelines at scale and a cross-functional presentation where you walk through a past project in detail. Some roles add a paper review or research discussion.
Where can I find and apply to computer vision engineer jobs?
You can find and apply to computer vision engineer jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your background and specialization, then apply directly to each one that fits.
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