Computer Vision Engineer

A startup company in the Insurance Tech field has developed an innovative digital insurance platform that is revolutionizing the purchasing, underwriting, and management of insurance for shared homes in the US.

The development features an online and fast process for comparing insurance quotes and selecting the appropriate insurance in less than five minutes, driven by data and AI-based. The platform is available for private customers (B2C) and insurance agents across the US (B2B).

Located in the Sharon area, employees work 1-2 days from home, and the company has about 70 employees.

This is a stable company experiencing rapid growth with paying customers, having recently completed Series B funding and raised over $55M to date!

The role involves working as part of a team, from Research to Production in the fields of Computer Vision and Deep Learning in a GCP, AWS, MLOps, Pytorch, Tensorflow environment. This role will utilize research capabilities and understanding, alongside involvement in deployment, working in a Cloud environment, and more. The company is also planning to expand into NLP projects soon.

What You’ll Do:

• Develop machine learning and computer vision algorithms.

• Research algorithmic techniques that apply the field.

• Own data science processes end-to-end: from data collection, sanitizing, and preprocessing, through model training and development, all the way to deploying models to production.

• Design and building machine learning pipelines infrastructure for production and research alike.

Basic Requirements

• M.Sc./Ph.D. in Computer Science, Electrical Engineering or similar, with specialization in deep learning and computer vision.

• 5+ years hands-on experience in computer vision algorithms development, using Pytorch or Tensorflow.

• Ability to initiate and lead machine learning projects end-to-end.

• Experience in deployment of deep learning models- an advantage.

• Team player, positive, driven, independent and fast learner.

• Advantage: Structured data experience.

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