Machine Learning Engineer Intern contributing to AI solutions for financial services. Engaging in hands - on ML projects and real production issues in a hybrid working environment.
About the role
- Machine Learning Engineer establishing the technical stack and working on ecological data at Echo Labs. Building data pipelines, cloud infrastructure, and experiment tooling for ecological modelling.
Responsibilities
- 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.
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