Principle Applied Scientist - Computer VIsion
About Us:
We are an innovative company revolutionising retail checkout experiences by replacing traditional barcodes with cutting-edge Computer Vision technology. Our platform enables seamless, faster, and smarter checkout processes, enhancing the shopping experience for both retailers and consumers. We're growing rapidly and are looking for an experienced Android/Cross-Platform App Developer to join our team and help us build the future of retail technology.
Key Responsibilities:
Lead the research, design, and development of advanced computer vision models for tasks like object detection, tracking, segmentation, OCR, scene understanding, and 3D vision.
Translate business needs into scalable scientific solutions using state-of-the-art deep learning and classical computer vision techniques.
Design and implement experiments to evaluate performance, robustness, and accuracy of CV models in real-world production scenarios.
Collaborate with cross-functional teams including software engineering, product, and data teams to integrate vision models into applications
Drive innovation through internal IP generation (patents, publications) and contribute to the long-term AI/ML roadmap.
Provide scientific and technical leadership, mentoring junior scientists and reviewing designs and architectures.
Stay up to date with latest developments in AI, deep learning, and computer vision through academic and industrial research.e with industry best practices and emerging technologies to drive continuous improvement.
Qualifications:
M.S. or Ph.D. in Computer Science, Electrical Engineering, or a related field with specialization in Computer Vision or Machine Learning.
5+ years of hands-on experience in building and deploying production-grade computer vision models.
Strong theoretical background and applied experience in deep learning frameworks (e.g., PyTorch) and model architectures (e.g., CNNs, Vision Transformers, Diffusion Models).
Experience in working with large-scale datasets, training pipelines, and performance evaluation metrics.
Proficiency in Python and scientific computing libraries (e.g., NumPy, OpenCV, scikit-learn).
Experience with model optimization for edge deployment (ONNX, TensorRT, pruning/quantization) is a strong plus.
Strong written and verbal communication skills, with a track record of mentoring and collaboration.
Preferred Qualifications:
Experience with computer vision in real-time systems (e.g., AR/VR, robotics, automotive, surveillance).
Published research papers in top-tier conferences (CVPR, ICCV, NeurIPS, etc.).
Exposure to MLOps or ML model lifecycle in production environments.
Familiarity with cloud platforms (AWS/GCP/Azure) and containerization tools (Docker, Kubernetes) and basic bash scripting.