AI / Machine Learning Engineer (Computer Vision)
Budget: $25 – $50 USD
Work Type: Remote (U.S. only)
Employment Type: Contract
Compensation: $30–$50 per hour (depending on experience)
Start Date: Immediate — Project begins next week
We are looking for a skilled AI / ML Engineer to join our team and help build a high-accuracy biometric authentication system for a smart lock platform.
The goal of this project is to develop a facial recognition system with extremely low False Acceptance Rate (FAR < 0.01%), capable of identifying users accurately—even in challenging cases such as distinguishing identical twins.
Project Environment
- Training with large-scale biometric datasets such as MegaFace and mobile face recognition datasets
- Deployment architecture will be hybrid, supporting both edge devices (smart lock hardware) and cloud systems
Responsibilities
- Develop and train machine learning / deep learning models for face recognition
- Build and improve computer vision pipelines for identity verification
- Work with large-scale datasets to improve accuracy and reliability
- Optimize models for real-world deployment on edge devices and cloud systems
Requirements
- Strong experience in Machine Learning / Deep Learning
- Hands-on experience with Computer Vision
- Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
- Experience working with large datasets and model training
Preferred
- Experience with face recognition systems
- Familiarity with models such as ArcFace, FaceNet, or similar
- Experience withLLMs or multimodal AI systems
Application
Please send:
- Your resume
- A short summary of your past Computer Vision or LLM projects
- Any GitHub, portfolio, or relevant work (if available)
We are prioritizing candidates who are available to start immediately, as the project will begin next week.
Employment Type: Contract
Compensation: $30–$50 per hour (depending on experience)
Start Date: Immediate — Project begins next week
We are looking for a skilled AI / ML Engineer to join our team and help build a high-accuracy biometric authentication system for a smart lock platform.
The goal of this project is to develop a facial recognition system with extremely low False Acceptance Rate (FAR < 0.01%), capable of identifying users accurately—even in challenging cases such as distinguishing identical twins.
Project Environment
- Training with large-scale biometric datasets such as MegaFace and mobile face recognition datasets
- Deployment architecture will be hybrid, supporting both edge devices (smart lock hardware) and cloud systems
Responsibilities
- Develop and train machine learning / deep learning models for face recognition
- Build and improve computer vision pipelines for identity verification
- Work with large-scale datasets to improve accuracy and reliability
- Optimize models for real-world deployment on edge devices and cloud systems
Requirements
- Strong experience in Machine Learning / Deep Learning
- Hands-on experience with Computer Vision
- Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
- Experience working with large datasets and model training
Preferred
- Experience with face recognition systems
- Familiarity with models such as ArcFace, FaceNet, or similar
- Experience withLLMs or multimodal AI systems
Application
Please send:
- Your resume
- A short summary of your past Computer Vision or LLM projects
- Any GitHub, portfolio, or relevant work (if available)
We are prioritizing candidates who are available to start immediately, as the project will begin next week.