At Jua we are building it now. Our models already run on four continents and help power the energy buildout needed to support ever larger intelligence.

We are assembling a world-class team of researchers and entrepreneurial minds to turn this model into reality at global scale. If you want the opportunity of your life, come build physical AI with us.

What we’re looking for

We’re looking for an exceptional and ambitious Data Engineering Intern who’s eager to learn fast, take ownership, and contribute to real production systems.

You’ll work together with our experienced data and AI engineers and support expanding and refining Jua’s data infrastructure, focusing on data acquisition, validation, and integration using Dagster, Python, and modern cloud technologies. Your work will directly accelerate our ability to onboard new datasets and improve model performance.

Internship duration: 3 months (extendable to 6 months)

Responsibilities and tasks

Integrate and onboard new weather, climate, and geospatial datasets into Jua’s data infrastructure
Build and maintain robust data pipelines using Dagster, ensuring reliability and scalability
Collaborate with engineers and scientists to validate data quality, completeness, and usability
Contribute to documentation and automation that improve data ingestion and processing workflows
Support hindcast generation and other foundational data operations to increase engineering throughput
Learn directly from experienced mentors while contributing to production-grade systems that power Jua’s AI models

Need-to-have

Currently pursuing or recently completed a Bachelor’s or Master’s degree in Computational Science, Engineering, Physics, Atmospheric or Earth Science
- or a proof that you are exceptional in your field without a degree (show us your projects on Github)
Strong programming skills in Python and familiarity with Git
High intellect, curiosity, strong attention to detail, and a drive to excel in a fast-paced environment where intensity, long hours, and high standards are the norm

Nice-to-have

Understanding of data pipelines, ETL processes, and workflow orchestration tools such as Dagster, Airflow, or Prefect
Basic familiarity with GCP or AWS, and experience handling structured or geospatial data
Familiarity with Docker or containerized development
Knowledge of SQL and basic data modeling concepts
Interest in machine learning or large-scale simulation systems

At the end of your internship, you will have

Built and deployed data pipelines used in production at Jua
Onboarded new datasets that enhance model accuracy and experimentation speed
Gained hands-on experience with Dagster, cloud infrastructure, and collaborative data workflows
Contributed directly to Jua’s mission to redefine how the world forecasts and understands the physical world
Demonstrated exceptional ownership, technical skill, and impact

About you

You’re technically strong, curious, and eager to learn fast
You take initiative and enjoy solving open-ended problems
You thrive in an environment where expectations are high and feedback is direct
You’re excited by the idea of working with world-class engineers and scientists on real-world data challenges

Please note: We prioritise your answers to the application questions over your CV, so we encourage you to complete them as thoughtfully and thoroughly as possible.

At Jua, we foster a performance culture and value people who embody our beliefs of service and adventure. We prioritize agility, operating at the highest clock speed to adapt quickly to change.

We innovate on behalf of our users and leverage data supremacy to maintain our competitive edge. Through clear communication and fact-based decision-making, we ensure alignment in our pursuit of excellence.

With these principles, we aim to create a customer-focused, value-centric organization that sets new standards in the industry. We value the unique perspectives that each individual brings to the table and believe that embracing diverse backgrounds and experiences enriches our collective journey towards growth and success.

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