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'll 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!
Your Foundation:
- You want to solve real-world problems and get things done.
- Ph.D. in Computer Science or Machine Learning or related technical discipline, or related practical experience.
- 3+ years experience in designing ML algorithm solutions
- 2+ graph-based machine learning papers at top venues such as NeurIPS, ICLR, ICML, etc
- Well experienced in Python and its deep learning framework ecosystem, such as PyTorch
Your Extra Special Sauce:
- Passionate about OSS, including various contributions
- Experience developing large-scale systems
- Ability to diagnose technical problems, debug code, and automate routine tasks
- Analytical approach coupled with solid communication skills and a sense of ownership
Benefits
- Stock
- Competitive Salaries
- Medical, dental insurance
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
Top Skills
What We Do
Democratizing AI on the Modern Data Stack!
The team behind PyG (PyG.org) is working on a turn-key solution for AI over large scale data warehouses. We believe the future of ML is a seamless integration between modern cloud data warehouses and AI algorithms. Our ML infrastructure massively simplifies the training and deployment of ML models on complex data.
With over 40,000 monthly downloads and nearly 13,000 Github stars, PyG is the ultimate platform for training and development of Graph Neural Network (GNN) architectures. GNNs -- one of the hottest areas of machine learning now -- are a class of deep learning models that generalize Transformer and CNN architectures and enable us to apply the power of deep learning to complex data. GNNs are unique in a sense that they can be applied to data of different shapes and modalities.
