Computer Vision/Machine Learning Engineer

Glint Tech Solutions • California, CA

57d

About The Position

About the Role We are looking for a Computer Vision / Machine Learning Engineer to join a fast-growing, product-driven team working on real-world AI applications. This role focuses on building and optimizing lightweight visual models for production environments, with strong emphasis on performance, efficiency, and deployment. What You'll Do Model Selection 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

Requirements

• Bachelor's degree with 3+ years of experience, OR Master's degree with 1+ year of experience, OR PhD in a related field
• 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 Haves

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

Responsibilities

• Model Selection 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

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level