Machine Learning

Note: This is a full-time contractor role, and we require that they do not take up any other employment while working with us. Role Overview We are seeking a highly skilled and motivated Machine Learning/Computer Vision Engineer to join our AI team. The core focus of this role will be the end-to-end development and deployment of deep learning models for advanced visual understanding. This includes a strong emphasis on Computer Vision tasks such as Object Detection, Semantic Segmentation, and Instance Segmentation. You will be responsible for translating cutting-edge research into robust, scalable, production-grade systems in a Python-centric environment. Key Responsibilities ● Model Design and Development: Research, design, implement, and optimize state-of-the-art deep learning models specifically for computer vision segmentation and object detection algorithms. ● ML Lifecycle Management: Own the full machine learning lifecycle, from data collection and annotation to training, evaluation, validation, and production deployment. ● Coding & Integration: Write clean, efficient, and well-documented production code in Python, utilizing key ML/CV libraries and frameworks. ● System Integration: Collaborate with software and platform engineers to seamlessly integrate computer vision capabilities into our core products and infrastructure. ● Performance Optimization: Evaluate model performance, benchmark speed and accuracy, and optimize models for inference latency and memory consumption on target hardware (cloud or edge). ● Research & Innovation: Stay abreast of the latest academic and industry advancements in deep learning and computer vision to propose and implement innovative solutions. ● Collaboration: Work closely with data scientists, software developers, and product managers to define requirements and deliver high-impact features. Required Qualifications ● Experience: A minimum of 3+ years of professional experience in a Machine Learning Engineer, Computer Vision Engineer, or similar role. ● Programming: Strong proficiency in Python and its scientific computing stack (e.g., NumPy, Pandas). ● Computer Vision: Proven practical experience with computer vision algorithms and deep learning techniques for Object Detection (e.g., YOLO, Faster R-CNN) and Segmentation (Semantic or Instance). ● Deep Learning Frameworks: Expertise in at least one major deep learning framework (PyTorch or TensorFlow/Keras). ● Tooling: Hands-on experience with computer vision libraries such as OpenCV. ● Foundational Knowledge: Solid understanding of machine learning principles, neural network architectures (especially CNNs), and image processing fundamentals. ● Education: Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related technical/quantitative field. ● Proficiency in modern code management tools, especially Git. Preferred Qualifications (Nice-to-Haves) ● Experience with MLOps practices and tools (e.g., Docker, Kubernetes, experiment tracking, model serving). ● Familiarity with cloud computing services (AWS, Google Cloud Platform, or Azure) for model training and deployment. ● Experience in optimizing models for performance and size (e.g., quantization, pruning, use of TensorRT/ONNX).