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.
What you will do!
- Design and implement complex, distributed platforms for storing data, and scaling parallel algorithms for graph learning workloads.
- Design systems that run across multiple warehouses (eg., Snowflake and Databricks) and multiple clouds (AWS, GCP, Azure).
- Develop generic and generalizable APIs and domain specific languages for big data processing and machine learning (deep learning, graph representation learning).
Your Foundation:
- 1+ years industry experience designing, building and supporting generic systems for large scale data analytics and deep learning in production.
- Experience building large scale distributed fault tolerant services.
- Experience building generalizable APIs and domain specific languages.
- Excellent understanding of low level operating systems concepts including multi-threading, memory management, networking and storage, performance and scale.
- Strong CS fundamentals including data structures, algorithms, and distributed systems.
- Systems programming skills including multi-threading, concurrency, etc. Fluency in C++ is preferred.
- Track record of identifying and implementing creative solutions.
- Experience with cloud infrastructure - AWS, Azure or Google Cloud.
- BS in Computer Science; Masters or PhD Preferred.
- Fluency in English.
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.
