Machine Learning Engineer - Molecular AI

Machine Learning Engineer (Molecular AI) About the Role We are looking for a Machine Learning Engineer to help build, deploy, and optimize machine learning systems for molecules and proteins. You will bridge cutting-edge ML research with production-ready software, ensuring that models are scalable, reliable, and efficiently integrated into scientific workflows. This role is ideal for someone who enjoys making ML systems work in practice—from training and optimization to deployment and production monitoring—while collaborating closely with ML scientists and computational chemists. Responsibilities * Build, deploy, and maintain machine learning models for molecular and protein applications. * Develop scalable ML infrastructure and production pipelines for training, inference,and evaluation. * Optimize model performance, computational efficiency, and resource utilization. * Design robust data pipelines for molecular and biological datasets. * Collaborate with ML scientists to productionize new algorithms and research prototypes. * Implement best practices for testing, monitoring, reproducibility, and MLOps. * Improve inference speed, distributed training, and deployment across cloud and HPC environments. * Work closely with multidisciplinary teams including computational chemistry,structural biology, and software engineering. Qualifications Required * MSc or PhD in Computer Science, Machine Learning, Engineering, Computational Biology, or a related field (or equivalent industry experience). * Strong software engineering skills in Python. * Experience with PyTorch and modern ML frameworks. * Experience deploying machine learning models into production environments. * Knowledge of MLOps, containerization (Docker), cloud platforms, CI/CD, and model serving. * Experience optimizing model training and inference for performance and scalability. * Strong problem-solving skills and the ability to work independently in a fast-moving environment. Nice to Have * Experience working with molecular, protein, structural biology, or chemistry datasets * Familiarity with graph neural networks, geometric deep learning, or transformer-based models. * Experience with distributed training, GPU optimization, or high-performance * computing. * Knowledge of molecular representations, protein structures, or computational chemistry.