Junior PyTorch And Scikit-learn Deep Learning Research Engineer

What role will you play in the team

You will play a foundational role in our core machine learning research division, driving algorithmic innovation and developing cutting-edge predictive models. Your primary focus will be designing, training, and optimizing deep neural networks to solve complex unstructured data problems, directly contributing to our enterprise artificial intelligence roadmap and advanced technological capabilities.

What you will do

You will collaborate closely with senior data scientists and domain experts to translate raw datasets into high-performing machine learning solutions. Your daily activities will involve feature engineering, hyperparameter tuning, model validation, and conducting rigorous empirical evaluations using advanced Python frameworks to ensure maximum predictive accuracy and operational efficiency.

Key responsibility

• Design, architect, and implement robust machine learning pipelines utilizing Python, scikit-learn, and PyTorch for scalable model training and deployment.
• Perform comprehensive data preprocessing, exploratory data analysis, and feature selection on large-scale enterprise datasets.
• Conduct exhaustive hyperparameter optimization and cross-validation experiments to mitigate overfitting and enhance generalization capabilities.
• Collaborate with software engineering teams to integrate trained models into production-grade microservices and real-time inference endpoints.
• Document experimental methodologies, model performance metrics, and architectural decisions to maintain transparency across the data science organization.
• Monitor production model drift, retrain algorithms proactively, and execute continuous integration pipelines for sustained predictive reliability.

Required Qualification and Skills

• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a closely related quantitative discipline with strong academic credentials.
• Demonstrated hands-on experience of 0-1 years in developing machine learning models using Python, PyTorch, and scikit-learn libraries.
• Strong foundational knowledge in linear algebra, multivariable calculus, probability theory, and fundamental statistical hypothesis testing.
• Proficiency in querying relational databases using SQL and managing dataframes with Pandas and NumPy.
• Familiarity with version control systems such as Git and containerization technologies like Docker for reproducible research environments.
• Exceptional analytical thinking, problem-solving aptitude, and clear technical communication skills for collaborative cross-functional execution.

Benefits Included

Competitive base salary structured to reward technical excellence and rapid professional growth within the artificial intelligence sector.
Comprehensive health insurance coverage encompassing medical, dental, and vision care for employee dependents.
Flexible working hours and structured remote work allowances to support a healthy work-life balance.
Dedicated annual learning and development budget for attending international machine learning conferences and obtaining specialized certifications.
Generous paid time off, mental health days, and well-being programs sponsored by the corporate wellness team.

A Day in the Life

Your day begins by reviewing overnight model training logs and evaluating validation loss curves in PyTorch. You will then participate in a daily stand-up with the data engineering squad to resolve API integration bottlenecks. The afternoon is dedicated to writing custom feature extraction scripts in scikit-learn and conducting peer code reviews before finalizing your daily experimental documentation.