What you’ll be doing: • Architect and Build AI-guilded Systems: Drive the design of and implementation of deep learning based models for protein structure prediction, protein–ligand/peptide interaction modeling, and molecular surface simulations. Use AlphaFold, RF Diffusion, graph neural networks (GNNs), variational autoencoders (VAEs), diffusion models, and transformer-based architectures to generate novel peptide and small-molecule candidates. • Build Data and Analytics Center of Excellence: Contribute to building scalable computational pipelines that combine multi-omics data, protein structure databases, and high-throughput screening datasets. • Elevate Software Engineering Standards: Provide software development, including algorithm programming and computational tool support to enhance the company’s medicinal chemistry platform, ensuring seamless integration of AI models into discovery workflows. Design and implement AI-driven virtual screening workflows that integrate neural networks, reinforcement learning, and generative modeling. • Lead, Mentor, and Innovate: Collaborate with medicinal chemistry and structural biology teams to translate AI-driven predictions into experimental validation and drug design. Prepare technical reports, contribute to patents, and present at top- tier scientific and industry conferences.

Qualifications: • Master’s degree or above (M.S. / Ph.D.) in Computer Science, Artificial Intelligence, Computational Chemistry, Structural Biology, or related fields. • 3+ years of relevant work experience in AI-driven molecular design or computational drug discovery. Proven expertise in neural networks, machine learning, and generative models applied to drug discovery (exceptional candidates with less experience may also be considered). • Hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) and model architectures such as transformers, GNNs, VAEs, diffusion models. • Familiarity with virtual screening platforms (e.g., Schrödinger Glide, MOE, Rosetta, AutoDock) and expertise in protein–ligand docking, protein structure prediction (AlphaFold/AlphaFold-Multimer), RF Diffusion, and molecular dynamics simulations. • Strong programming skills in Python and ability to integrate ML pipelines into scalable research workflows. • Experience in molecular glues or targeted protein degradation is highly desirable. • Strong cross-disciplinary communication and collaboration skills.