Machine Learning Engineering Summer 2026 Internship¶
Location: Remote (Global)
Time Commitment: ~20 hours/week, 5-6 months
Type: Unpaid Internship
Position Overview¶
We are seeking a Machine Learning Engineering Intern to join our research team. This role focuses on the practical application of classical machine learning and statistical modeling to solve problems in neural data analysis and system identification. You will work closely with Data Scientists and Neuroscientists to automate data processing and develop predictive models that help bridge the gap between biological observations and computational simulations.
Key Responsibilities¶
- Deploy and tune classical ML models (Random Forests, Gradient Boosting, SVMs) to classify neural states and predict signal trends
- Extract meaningful features from high-dimensional electrophysiological data
- Develop automated pipelines to clean and normalize large-scale datasets
- Perform rigorous cross-validation and error analysis
- Use unsupervised techniques (PCA, K-means) to identify patterns in neural firing populations
- Maintain high-quality, well-documented code
Qualifications¶
- Currently pursuing a degree in Computer Science, Data Science, Statistics, or related quantitative field
- Strong proficiency in Python and standard ML libraries (scikit-learn, NumPy, pandas)
- Solid understanding of probability, statistics, and ML mathematical foundations
- Experience working with time-series data or large structured datasets
- Ability to translate research questions into structured ML tasks
- Capable of explaining model choices to a multidisciplinary team
Apply Now¶
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