Own and improve our ML training and evaluation pipelines, making them more reliable, flexible, and easier to extend
Improve configuration, maintainability, and traceability of our ML workflows (e.g., parameter handling, experiment tracking, pipeline design)
Reduce complexity and improve reliability in our Google Earth Engine (GEE) feature pipeline
Contribute to the development of our geospatial data infrastructure, extending our capacity to store and process vector files, as well as high-resolution raster datasets
Own and improve our CI/CD and testing strategy, supporting integration tests, dependency upgrades, and stronger release hygiene
Support with further development of our GCP infrastructure, adapting our architecture, monitoring and access management to evolving needs

A degree in Software Engineering, Computer Science, or a related field, with strong foundational knowledge in software development
3+ years (mid-level) or 5+ years (senior) of experience in engineering roles building backend applications and pipelines for data
or ML-intensive systems
Full proficiency in Python, solid knowledge of SQL, and working knowledge of orchestration tools such as Airflow
Experience with production ML systems, including training and evaluation pipelines, and collaboration with data scientists
Familiarity with cloud platforms (ideally GCP), including infrastructure-as-code, IAM, and system monitoring
Strong experience in software development lifecycle and operations (version control, testing, CI/CD)
A strong sense of ownership and ability to work independently in a fast-paced, collaborative environment, comfortable navigating ambiguity and evolving priorities
Clear communication and a readiness to take initiative in technical design discussions
Motivation to contribute to climate action and sustainable development through a rapidly evolving technology space.

Seqana

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