As the Director of ML Engineering, you will spearhead the development and implementation of advanced deep learning models for drug discovery. You will collaborate closely with computational chemists, structural biologists, and cheminformatics experts to design scalable ML solutions that power next-generation therapeutics. This role requires a strategic thinker with hands-on expertise in deep learning, molecular dynamics, and data-driven approaches to accelerate drug discovery pipelines.

Key Responsibilities:

• Lead the development of ML models for molecular property prediction, protein-ligand interactions, and de novo drug design.
• Apply deep learning techniques to problems in molecular dynamics, structural biology, and cheminformatics.
• Architect scalable ML pipelines using Python, TensorFlow/PyTorch, and cloud computing platforms.
• Collaborate with cross-functional teams, including medicinal chemists, computational biologists, and AI researchers, to integrate ML-driven insights into drug discovery.
• Drive innovation in AI-powered molecular simulation, structure-based drug design, and generative modeling for small molecules and biologics.
• Stay at the cutting edge of ML advancements and implement state-of-the-art methodologies in the biotech domain.
• Mentor and lead a team of ML engineers and researchers to foster technical excellence.

Required Qualifications:

• PhD in Computer Science, Computational Biology, Bioinformatics, or a related field with a focus on ML applications in drug discovery.
• 5+ years of experience developing and deploying ML models in the biotech or pharmaceutical industry.
• Expertise in deep learning frameworks (PyTorch, TensorFlow, JAX) and experience applying them to biological and chemical datasets.
• Strong proficiency in Python and familiarity with scientific computing libraries (NumPy, SciPy, RDKit).
• Experience with molecular dynamics simulations and structure-based drug discovery techniques.
• Knowledge of cheminformatics, structural biology, and bioinformatics methodologies.
• Proven track record of building ML models for applications like virtual screening, protein-ligand docking, and generative chemistry.
• Experience working with high-dimensional biological data, cloud computing, and scalable ML infrastructure.
• Strong leadership and communication skills, with the ability to work in a fast-paced, interdisciplinary environment.