The Value You'll Add:
- Design & Build: Help design the core systems for model training, inference, and scaling, working on everything from system architecture to APIs.
- Innovate: Contribute to the development of cutting-edge machine learning techniques tailored to work seamlessly with large-scale data warehouse systems.
- Collaborate: Work closely with engineering leaders and research scientists to bring new ideas to life and tackle real-world challenges.
- Deliver: Build and deploy production-quality systems that scale, ensuring the system is fast, reliable, and secure.
Your Foundation:
- Degree: Recent graduate with a BS in Computer Science, Engineering, or a related field. If you have an MS or Ph.D., that’s a bonus!
- Skills: Strong foundation in software development, systems design, and algorithms. Proficiency in programming languages like Python, Java, or C++.
- Passion: A deep interest in machine learning and cloud technologies. You’re excited about building scalable, high-performance systems.
- Curiosity: An eagerness to learn and grow in an innovative, fast-paced environment.
Your Extra Special Sauce:
- Experience with cloud platforms like AWS, Azure, or GCP.
- Knowledge of distributed systems and databases.
- Exposure to machine learning frameworks like PyTorch or TensorFlow.
- Hands-on experience building APIs or microservices.
- Contributions to open-source projects, research papers, or university-based ML work.
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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.
