Model code and data form the foundation of an AI system. Scale’s leading end- to-end solutions for the ML lifecycle based on real-world data will continue to set the bar for the data-centric AI movement. On the Document AI team, we are focused on building the best information extraction document models for any document in the world.

Your core focus will be on document understanding use cases, specifically around information extraction and form understanding. You will be involved end-to-end from coordinating with operations to create high quality datasets to productionizing models for our customers. If you are excited about shaping the future of the data-centric AI movement, we would love to hear from you!

You will:

Apply state of the art models developed internally and from the community, use them in production to solve problems for our customers and data labelers.
Work with product and research teams to identify opportunities for improvement in our current product line and for enabling upcoming product lines.
Work closely with customers - some of the most sophisticated ML organizations in the world - to quickly prototype and build new deep learning models targeted at multi-modal content understanding problems.

Ideally you’d have:

2+ years of model training, deployment and maintenance in a production environment.
Strong skills in NLP and deep learning.
Solid background in algorithms, data structures, and object-oriented programming.

Nice to haves:

Experience with deep learning with large scale document processing.
Published research in areas of machine learning at major conferences and/or journals.
Experience working with cloud technology stack (eg. AWS or GCP) and developing machine learning models in a cloud environment
Strong programming skills in Python, experience in Tensorflow or PyTorch.
Strong written and verbal communication skills.

Machine Learning Engineer - Document & Enterprise AI