Establish the inner workings of Echo's technical stack.
Build and maintain the data pipelines, cloud infrastructure, and experiment tooling that power Echo's modelling work.
Collaborate with a Director of Modelling & Data Infrastructure and the CTO to turn ecological data into model-ready inputs.
Develop reproducible modelling pipelines as we iterate.
Create a data and modelling infrastructure that enables rapid iteration and learning for ecology.

Requirements

5 years in ML engineering, data engineering, or applied ML research.
Fluency in Python; experience with PyTorch or equivalent deep learning framework.
Experience building data pipelines: ETL, data validation, format standardization at non-trivial scale.
Working knowledge of cloud platforms (AWS or GCP): object storage, compute provisioning, basic networking.
Comfort with version control, CI/CD, and reproducible experiment workflows.
Ability to work independently on well-scoped tasks and flag blockers early.
Background in ecology, environmental science, Earth observation, or prior work with ecological datasets.
Experience with geospatial data: satellite imagery, raster processing, coordinate systems, STAC metadata.
Experience with audio/acoustic data processing or bioacoustic analysis tools.
Working knowledge of R.
Contributions to open-source scientific or ML software.

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonPyTorchETLdata validationformat standardizationcloud platformsAWSGCPRgeospatial data

Soft Skills

independent workproblem-solvingcollaborationcommunicationiterationlearning