Computer Vision Data Scientist

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.