What are the responsibilities and job description for the Data Infra Engineer position at Kumo?
Build the Future of AI Infrastructure with Kumo!
Companies invest millions in storing terabytes of data in data lakehouses, yet only a small fraction is leveraged for predictive insights. Traditional machine learning pipelines are slow and complex, requiring months of engineering effort for data preparation, feature engineering, and model training.
At Kumo, we are redefining AI infrastructure for data lakehouses, enabling businesses to harness the power of Graph Neural Networks with minimal effort. Our platform eliminates the complexities of traditional ML pipelines, allowing users to train high-performance models directly on their relational data with just a few lines of Predictive Query Language (PQL).
We are looking for Data Infrastructure Engineers to join our team and help build a scalable, high-performance ML platform. If you thrive in designing robust, cloud-native infrastructure, optimizing data pipelines, and building scalable services, we'd love to hear from you!
As a Data Infrastructure Engineer at Kumo, you will :
- Design and optimize scalable, cloud-native infrastructure for high-performance ML workloads.
- Develop and maintain efficient data ingestion pipelines and connectors for large-scale datasets.
- Build and enhance resilient ETL pipelines to transform, process, and store data for analytics and ML.
- Implement best practices for data security, governance, and sharing within distributed environments.
- Optimize performance of data processing frameworks, including Spark, Presto, and Hive.
- Automate deployment of infrastructure using Kubernetes, Terraform, and CI / CD tools.
- Work closely with data scientists and ML engineers to bridge infrastructure with machine learning applications.
Your Foundation :
Bonus Points :
Why Join Kumo?
Ready to build the next-gen AI infrastructure? Apply today!
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.
