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Sr. Deep Learning Engineer

Sr. Deep Learning Engineer

Work location: Worldwide
Work arrangement: Remote
Salary range: US$ 40,000 - US$ 150,000 per annum
Skills:
Problem Solving
Artificial Intelligence
Critical Thinking
Neural Networks
Clean Code

Company Description

Friendly is a robotic cognition company specializing in workflow automation solutions for the Insurance and Financial Services industries. Our deep learning platform transforms the digital landscape by digitizing complex documents, facilitating claim auto-adjudication, and enabling seamless processing of new business. We have established partnerships with leading insurance carriers and are achieving remarkable outcomes. Join us in shaping the future!

Role Description

This is a full-time remote role for a Deep Learning Engineer at Friendly. As a Deep Learning Engineer, you will be responsible for developing and implementing advanced algorithms and models for our deep learning platform. Your day-to-day tasks will involve designing and training deep neural networks, optimizing models for performance and scalability, conducting research to explore and incorporate the latest advancements in deep learning, and collaborating with cross-functional teams to enhance our solutions.

Qualifications

  • Strong proficiency in deep learning frameworks such as TensorFlow, PyTorch, or Keras

  • Expertise in designing and training deep neural networks for various tasks

  • Experience in developing and implementing advanced machine learning algorithms

  • Proficiency in programming languages such as Python, C++, or Java

  • Understanding of computer vision, natural language processing, or reinforcement learning

  • Knowledge of data preprocessing, feature engineering, and model evaluation

  • Strong problem-solving and analytical skills

  • Excellent communication and collaboration skills

  • Ability to work independently and remotely

  • Master's or PhD in Computer Science, Artificial Intelligence, or related field

This application includes an assessment as the first step