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# Computer Vision/Machine Learning Engineer

- **Company:** GLINT TECH SOLUTIONS LLC
- **Location:** United States
- **Salary:** $150,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Application Performance Management, Computer Vision, Program Optimization, Python (Programming Language), Machine Learning, Performance Tuning, Tensorflow, Real Time Systems, Pytorch, Deep Learning, Low Latency, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations
- **Published:** May 20, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=80df946c65e85d07

## About the Role

Do you have experience in Model deployment?, Do you have a Master's degree?, * Bachelor's degree with 3+ years of experience, OR

 * Master's degree with 1+ year of experience, OR
 * PhD in a related field

Technical Skills

 * Strong foundation in Computer Vision and Machine Learning
 * Experience with model optimization for production (latency, memory, power trade-offs)
 * Hands-on experience with lightweight model deployment (e.g., ONNX, TFLite, CoreML)
 * Experience with model compression techniques (quantization, pruning, distillation)
 * Solid programming skills (Python + ML frameworks such as PyTorch or TensorFlow), * Experience building and deploying models in real-world production environments
 * Familiar with edge/mobile AI scenarios or performance-constrained systems, * Strong problem-solving ability and ownership mindset
 * Comfortable working in a fast-paced, high-growth environment
 * Able to independently drive projects end-to-end

Nice to Have

 * Experience with mobile/edge deployment (iOS / Android)
 * Experience optimizing models for real-time applications
 * Background in performance-critical systems

## Description

We are looking for a Computer Vision / Machine Learning Engineer to join a fast-growing, product-driven team working on real-world AI applications., Choose the most suitable approaches (traditional algorithms vs. deep learning) based on real-world use cases and constraints

 * Performance Optimization
Optimize models by balancing accuracy, latency, memory, and power consumption

 * Model Training & Compression
Train and optimize lightweight models using techniques such as quantization, pruning, and knowledge distillation

 * Deployment & Optimization
Deploy models to production environments (mobile/edge), ensuring low latency and high efficiency

 * End-to-End Ownership
Drive the full lifecycle from data analysis model development optimization deployment

 * Application Performance
Improve overall application responsiveness and resource usage to ensure smooth user experience


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