Computer Vision / Machine Learning Engineer

Project Overview:

We are looking for a highly skilled and motivated Computer Vision / Machine Learning Engineer to join our AI team and lead model improvement initiatives for our already-deployed mobile application. The app is designed to assist amputees by detecting hand, finger, or limb anomalies in real time using computer vision. Your role will be critical in enhancing the accuracy, robustness, and real-world performance of our anomaly detection models, particularly across diverse environments and user types.

Customer Overview:

Our client is a leading healthcare company, dedicated to improving patient outcomes through innovative medical solutions and high-quality care.

Responsibilities:

• Analyze the performance of existing CV/ML models in production to identify failure modes, bias, or performance gaps.
• Improve anomaly detection models for hands, fingers, and limb-related irregularities using real-world data.
• Experiment with model architectures, fine-tuning, and transfer learning approaches to improve detection accuracy and reduce false positives/negatives.
• Optimize models for real-time inference on mobile/edge devices (e.g. TensorFlow Lite, CoreML, ONNX).
• Collaborate with data scientists, app developers, and clinical advisors to ensure model outputs are reliable and actionable.
• Design and implement data collection, annotation, and augmentation pipelines tailored for prosthetic/amputee scenarios.
• Conduct A/B testing and model validation in live environments with real user feedback.
• Monitor deployed models using telemetry and feedback to drive continuous improvements.

Skills/Requirements:

• Proven experience (3+ years) in building and optimizing computer vision models for image classification, object detection, or anomaly detection.
• Proficiency in Python and major ML/CV frameworks (TensorFlow, PyTorch, OpenCV).
• Experience working with medical imaging, biomedical signals, or human body detection is a strong plus.
• Solid understanding of model evaluation techniques and metrics, particularly in healthcare or safety-critical applications.
• Experience deploying models to production, preferably on mobile or embedded systems.
• Familiarity with data annotation tools and managing datasets with edge cases (e.g. partial limbs, occlusions).
• Excellent problem-solving skills and the ability to work independently or in a small, fast-paced team
• (Preferred) Experience with 3D hand pose estimation, depth sensing, or synthetic data generation