Come and change the world of AI with the Kumo team!
Companies spend millions of dollars to store terabytes of data in data lakehouses, but only leverage a fraction of it for predictive tasks. This is because traditional machine learning is slow and time consuming, taking months to perform feature engineering, build training pipelines, and achieve acceptable performance.
At Kumo, we are building a machine learning platform for data lakehouses, enabling data scientists to train powerful Graph Neural Net models directly on their relational data, with only a few lines of declarative syntax known as Predictive Query Language. The Kumo platform enables users to build models a dozen times faster, and achieve better model accuracy than traditional approaches.
In this role, you will help us design and build the core of our system working alongside seasoned engineering leaders with decades of experience and some of the best researchers from Stanford and other top universities. We are adapting and inventing cutting edge ML techniques to fit well with learning over data warehouses. The technical challenge here is to create scalable storage and compute systems that will fit well with both the data warehouse and the ML training and inference systems and provide for scalability, reliability, restart and security of cloud-first applications. The final product will follow a customer first approach and be extremely easy to use from both ML and cloud deployment.
The Value You'll Add:
- Design the core of our training and inference systems
- Design and build systems to scale and integrate
- Design APIs between the system components to decouple them and make independent development easy
- Produce designs that can be iterated over time to achieve more scalability
- Implement the first version of the product
- Engage with customers, iterate on the product
Your Foundation:
- BS (preferred MS or Ph.D.) in Computer Science or related technical discipline, or related practical experience
- Role will be focused on Systems/Scalability/Integrations.
- 2+ years experience in software design, development, and algorithm-related solutions
- 2+ years experience programming in OOP languages like Java, Python, or C++
Your Extra Special Sauce:
- Knowledge of Cloud distributed storage/databases, file systems, and distributed storage.
- Ideal candidate would have the knowledge to integrate systems and distributed data processing technologies
- Experience building Micro-services & Cloud Platforms on AWS, and Azure.
- Experience with industry, open-source projects, and/or academic research in large-data, parallel, and distributed systems.
- Experience in using and/or contributing to an inference serving technology, PyTorch, TensorFlow, etc.
- Experience in building machine learning platforms at large scale
- Understand the fundamentals of machine learning, ideally in both academic and industry environments
Benefits
- Stock
- Competitive Salaries
- Medical Insurance
- 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.
