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!
\n- 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.
- 1 years of experience as an Infrastructure Engineer, Data Engineer, or related role in SaaS/Enterprise environments.
- Strong expertise in building, scaling, and maintaining cloud infrastructure (AWS, GCP, or Azure).
- Hands-on experience with data storage, ingestion, and processing in distributed environments.
- Proficiency in ETL development and building high-performance data pipelines.
- Solid understanding of databases, storage formats (Parquet, Avro, Arrow, JSON), and schema designs.
- Experience working with orchestration tools such as Temporal, Airflow, or Luigi.
- Strong programming skills in Python, Scala, or Java.
- Knowledge of containerization and orchestration (Docker, Kubernetes).
- Experience with Infrastructure as Code (Terraform, CloudFormation, Pulumi).
- Ability to debug performance bottlenecks and optimize distributed computing workloads.
- Excellent communication skills, with the ability to collaborate effectively across teams.
- Expertise in Spark, Presto, or Hive for large-scale data processing.
- Experience with serverless architectures and event-driven processing (AWS Lambda, Kinesis, Kafka).
- Familiarity with Databricks, Azure Data Factory (ADF), or cloud ML solutions.
- Understanding of high-availability, fault tolerance, and observability in cloud environments.
- Be part of a cutting-edge AI and ML infrastructure team revolutionizing how companies leverage their data.
- Work with top engineers and data scientists on solving complex, large-scale infrastructure challenges.
- Competitive salary, equity, and benefits in a fast-growing AI company.
- Flexible work environment with opportunities to shape the future of AI-powered data platforms.
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.
