Research Intern – AI for Earth Observation

NorthStar Earth & Space

. Take ownership of the tree species classification pipeline and reference data .

Posted 11/4/2026part-timeMontreal • CanadaEntry LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Python programming, particularly with libraries such as NumPy, pandas, and scikit-learn, while applying machine learning techniques for classification and model evaluation in remote sensing and forestry contexts. Proficient in delivering well-documented code and presenting analytical results effectively to scientific teams.

Highest-signal resume keywords

Python ProgrammingMachine LearningSatellite Data AnalysisGIS SkillsRemote Sensing

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonNumPyPandasScikit-LearnMachine LearningClassificationModel EvaluationGISPyTorchRemote Sensing

Soft Skills

Rigor in Evaluating ResultsPresentation SkillsCollaboration

Tools & Technologies

Sentinel-2Google Earth EngineRasterioGeopandasQGIS

Industry Keywords

ForestryVegetationRemote SensingGeomatics

Tech Stack

Tools & technologies

NumpyPandasPythonPyTorchRemote SensingScikit-Learn

About the role

Key responsibilities & impact

Take ownership of the tree species classification pipeline and reference data
Build a benchmark to compare Tessera and AlphaEarth embeddings with the current pipeline
Train and evaluate simple classifiers, including random forests and a small neural network
Measure performance based on the amount of labeled data used, from 5% to 100%
Test the impact of mixed forest stand polygons
Present results by species, with particular attention to rare species
Deliver clean, well-documented code integrated into the repository
Produce a short final report and presentation for the team
Work directly with the EI² team’s scientists and data scientists

Requirements

What you’ll need

Master’s student in remote sensing, geomatics, forestry, machine learning, or a related field
Solid Python skills, including NumPy, pandas, and scikit-learn
Knowledge of machine learning, including classification and model evaluation
Experience with satellite data, ideally Sentinel-2 or Google Earth Engine
Basic GIS skills, including rasterio, geopandas, or QGIS
Rigor in evaluating results
Ability to work in English
Basic PyTorch knowledge (an asset)
Background in forestry or vegetation (an asset)
French proficiency to work with MRNF documents (an asset)
CV and a brief example of a remote sensing or machine learning project required to apply

Benefits

Comp & perks

Paid internship
Flexible hours
Hybrid work model
Mentoring from experienced scientists and data scientists
Concrete project whose results will directly inform the team’s technical choices
Office ideally located in Old Montreal
Safe and inclusive work environment