Description:

Join us at the heart of the Kumo Graph Neural Network and Relational Deep Learning architecture, where you'll play a pivotal role in shaping and expanding its capabilities.
You will drive innovative designs that enhance our GNN backbone and integrate cutting-edge temporal learning strategies.
Your work will bridge the gap between foundational models and GNNs, tackling diverse use cases like recommendation systems, customer retention, forecasting, and fraud detection.
Leverage your expertise in machine learning and AI to solve critical challenges, creating scalable and adaptable solutions that push the boundaries of what’s possible.
Get ready to make a significant impact in a dynamic and exciting environment!

Requirements:

You want to solve real-world problems and get things done.
A Ph.D. in Computer Science or Machine Learning or a related technical discipline, or related practical experience is required.
You must have 3+ years of experience in designing ML algorithm solutions.
You should have 2+ graph-based machine learning papers published at top venues such as NeurIPS, ICLR, ICML, etc.
Well-experienced in Python and its deep learning framework ecosystem, such as PyTorch.