Overview

Machine Learning Engineer (Python)

Responsibilities

• Core Machine Learning Development
• Develop and optimize machine learning models using Python and frameworks such as scikit-learn. Basic hands-on experience with deep learning frameworks such as PyTorch or TensorFlow is preferred.
• Apply supervised and unsupervised learning techniques to real-world problems.
• Good understanding of Regression, Classification, and Clustering algorithms. Understand model evaluation techniques such as cross-validation, ROC-AUC, Precision, Recall, and F1-score.
• Perform feature engineering, feature selection, data preprocessing, normalization, and scaling.
• Understand model training, validation, hyperparameter tuning, and performance optimization.
• Ability to analyze model results and identify opportunities for improvement.
• Data Processing & Analysis
• Strong practical experience with Pandas and NumPy.
• Ability to clean, transform, analyze, and prepare raw datasets for machine learning.
• Experience working with structured datasets and building reusable data-processing workflows.
• Basic understanding of data quality, missing values, outliers, normalization, and feature preparation.
• Polars is optional.
• Experience with time-series data is optional/preferred.
• Deep Learning & Advanced ML (Preferred)
• Basic practical understanding of neural networks and deep learning concepts.
• Hands-on experience with CNN, RNN, or LSTM is preferred but not mandatory.
• Ability to understand and work with existing deep learning models.
• Experience with time-series modeling, signal processing, peak/event detection, or waveform data is optional.
• Experience with scientific or instrument-generated datasets, including mass spectrometry data, is a plus but not mandatory.
• Model Deployment & Software Engineering
• Develop clean, modular, and production-ready Python code.
• Basic hands-on experience with FastAPI, Flask, or Django for serving ML models is preferred.
• Understand how to expose trained models through APIs.
• Experience with Git and collaborative software development workflows.
• Ability to write basic unit tests and maintain code quality.
• Basic Docker knowledge is preferred.
• MLOps (Optional)
• Basic understanding of the machine learning lifecycle.
• Exposure to MLflow, DVC, or similar experiment/model tracking tools is a plus.

Qualifications

• 2 years of hands-on experience in Python and Machine Learning.
• Strong foundation in classical Machine Learning, practical model development, data preprocessing, feature engineering, model training, and evaluation.
• Technical Stack
• Proficiency in Python, Pandas, and NumPy. Practical experience with at least one end-to-end ML project.
• Software Engineering
• Good understanding of software engineering practices, Git, and clean Python development.
• Optional / Plus
• Deep Learning, MLOps, time-series modeling, signal processing, and scientific/mass-spectrometry data experience.