Endovision is dedicated to enhancing the diagnostic accuracy of upper GI endoscopists through the use of deep learning and computational modeling, ultimately contributing to improved healthcare outcomes. As a Data Science Intern based in New Delhi, you will play a crucial role in developing end-to-end machine learning models to address real-world challenges within the industry. In this on-site internship position, you will work on implementing cutting-edge AI technologies, leveraging state-of-the-art research papers, and contributing to the company's intellectual property. You will be involved in building AI-first products and collaborating with research scientists and engineers at Endovision and its partner institutions. The primary focus areas will include deep learning, computer vision, and graphics, particularly in the context of endoscopy. Ideal candidates for this role should possess experience in Computer Vision and Deep Learning, with a strong foundation in neural networks such as CNNs, RNNs, autoencoders, and transfer learning methods. Proficiency in Python and its scientific libraries, familiarity with Machine Learning techniques, and knowledge of Deep Learning frameworks like Keras, TensorFlow, and PyTorch are essential requirements. Strong analytical and problem-solving skills, along with excellent communication abilities, are key attributes for success in this role. Candidates pursuing a Bachelor's or Master's degree in Computer Science, Statistics, or a related field are encouraged to apply. Experience with end-to-end machine learning projects would be advantageous. Preferred candidates will have the ability to independently implement and evaluate ideas using modern deep learning tools like Python, PyTorch, and GPU-enabled compute. Additionally, having a track record of implementing or publishing research papers in reputable academic conferences related to deep learning and computer vision applications will be highly valued. A positive attitude, strong teamwork skills, and effective communication capabilities are essential qualities for individuals joining our dynamic team at Endovision.,
Endovision is a Med-tech company, which is helping the endoscopists to reduce cancer miss rate with the aid of real-time video analysis using AI. We are solving some of the hardest problems in the field of computer vision and deep learning and building end-to-end solutions for deep video understanding. Responsibilities We are looking to hire a new Research Engineer in our team. Your main responsibilities will be: Implementing state-of-the-art research papers, contributing to the company IP, and technology stack deployed in Nvidia embedded and dGPU Ecosystem. You’d work on cutting-edge deep learning, computer vision and spatio-temporal problems with an emphasis on endoscopy, with an opportunity to collaborate with research scientists and engineers at the Endovision and its partnering institutions. Implement/evaluate ideas, build and execute strategies to model hard video understanding problems, evaluate and benchmark end-to-end AI solutions in a collaborative team environment. Be part of and contribute to the evolution of AI Experimentation Lifecycle by focussing on experiment reusability and reproducibility. Requirements BSc/BA in Computer Science/ Eng., Engineering, Physics, Mathematics or relevant mathematically intensive fields Must have Computer vision/Image processing background with deep focus on deep learning (CNNs/GAN/vision transformers, etc.) Deep understanding in working of neural networks and its various aspects. Proven experience using statistical computer languages (R, Python, etc.) to manipulate data and draw insights from large data sets. Must have good experience with pytorch . Prior experience with Nvidia Holoscan and Nvidia tensorrt is a plus Experience in machine-learning and operations research (ability to read, understand and reproduce solutions from leading research papers of the area). Knowledge of a variety of machine learning techniques (deep learning architectures, clustering, decision tree learning, random forests, ensembles etc.) and their real-world advantages/drawbacks. Knowledge of best practices / most common mistakes in designing and implementing Machine Learning systems, mature instinct / intuition for real problem diagnostics and solving. Must have experience (academic or industry) with Computer Vision and Deep Learning in at least two of:- Neural networks - CNNs, RNNs, autoencoders, transfer learning, numerical optimization, etc. Comfortable working within an agile and iterative prototyping in startups.