ML Engineer (UAE)

Insilico Medicine is seeking a Machine Learning Engineer to develop, support and improve predictive models and retrosynthesis in Chemistry & Biology. The candidate will be writing production-level Python code, run ML-related experiments, integrate new functionalities, work with foundation models. The candidate will also provide technical support to the DevOps team in running the ML infrastructure and perform intrateam MLOps., * Develop and maintain the internal machine learning (ML) pipeline at the production level to support cutting-edge drug discovery initiatives.

• Optimize, refactor, and debug existing code in Python to enhance performance, scalability, and efficiency.
• Deploy ML models into the platform for real-world applications in chemistry and biology.
• Implement and fine-tune ML-dedicated algorithms in Python, ensuring high accuracy and robustness.
• Collaborate on MLOps practices to ensure seamless model integration, deployment, and continuous improvement.

Bachelor's degree/Master's degree/PhD degree in a Machine Learning related field., * Strong background in machine learning (ML) with practical application experience.

• 4+ years of experience in Python production-level development.
• Proficiency with coding standards such as PEP8, Google style guide, or similar best practices.
• Experience with NoSQL databases, such as MongoDB.
• Experience working with Linux or other Unix-based operating systems.
• Proficiency in version control systems like Git.
• Hands-on experience with Python numerical and machine learning libraries such as Numpy, Pandas, PyTorch, and Scikit-learn.
• Solid understanding of object-oriented programming (OOP), design patterns, and software architecture best practices.
• A proactive attitude, strong problem-solving skills, and a commitment to continuous learning.

III. Preferred Skills

• Experience with deep learning (DL) frameworks and techniques.
• Familiarity with RDKit for cheminformatics and Plotly for data visualization.
• Hands-on experience with a range of ML/DL methods such as Transformers, RNNs, CNNs, GNNs, and Gradient Boosting.
• Expertise in feature engineering and optimization techniques.
• Knowledge of cheminformatics and the drug discovery process.
• Ability to quickly learn and adapt to new libraries, tools, and emerging ML technologies.
• Experience in programming with C++ is an advantage