Mechine Learning Engineer (Internship)

About Matrice: Matrice AI is building a one-stop solution to build, deploy and manage AI applications. We help customers build applications with low or no code.Responsibilities:Develop and implement state-of-the-art computer vision models for image segmentation, object detection, and video classificationDesign and conduct experiments to improve model performance and efficiencyImplement action tracking algorithms for video processing applicationsCollaborate with the backend team to integrate ML models into the production environmentPreprocess and analyze large datasets for training computer vision modelsOptimize model performance for deployment on various hardware platformsStay up-to-date with the latest advancements in computer vision and deep learningContribute to the development of ML pipelines for model training, evaluation, and deploymentParticipate in code reviews and contribute to improving ML development practicesQualifications:Final year Computer Science graduate (BE/BTech/ME/MTech)Good knowledge of Operating Systems, Databases, and NetworkingPrevious industry experience in using Deep Learning algorithms for Computer Vision, especially in video processingFamiliarity with current SOTA computer vision models, including fundamental models like Segment Anything and CLIPCompleted deep learning courses from Coursera or DeepLearning.aiStrong understanding of Deep Learning model training on Computer Vision datasetsProficiency in Python and deep learning frameworks such as PyTorch or TensorFlowStrong fundamental knowledge of algorithms and data structuresExcellent troubleshooting and problem-solving skillsLearning mentality and openness to new technologiesBasic understanding of MongoDB, Git, and Docker Containerization (preferred)Knowledge in MERN stack or any other backend stack (preferred)Why Join Matrice?Join Matrice AI to work on cutting-edge AI and computer vision applications, gain valuable industry experience in machine learning, and be part of a team building innovative low-code/no-code solutions. PRB