Job Title

About QpiAI:QPIAI India Pvt. Ltd. is a next-generation technology company focused on Artificial Intelligence, Quantum Computing, and advanced IT innovation. At QPIAI, we believe that great ideas grow in the right environment. Our culture is built on flexibility, collaboration, and continuous learning, supported by a strong commitment to employee well-being and work–life balance. We provide a workplace that encourages creativity, fosters professional growth, and empowers people to take ownership of impactful projects.Role OverviewQpiAI Pharma is an AI-driven computational drug discovery platform integrating cheminformatics, structural biology, systems biology, machine learning, quantum computing, and scalable scientific workflows to accelerate the identification of novel therapeutics.We are seeking Senior Computational Drug Discovery Scientists to design, implement, validate, and productionize core scientific workflows for both small molecule and target discovery programs. This role requires deep scientific expertise combined with platform-oriented systems thinking to translate research into production-grade pipelines.Key Responsibilities1. Small Molecule Drug Design Workflows (LBDD/SBDD)Lead and implement workflows for de novo design, virtual screening, molecular docking, molecular dynamics (MD) simulations, FEP, QSAR/QSPR, and pharmacophore modeling, leveraging open-source packages (AutoDock Vina, GROMACS, LAMMPS, deep-learning accelerated solvers).Design, validate, and productionize screening pipelines for hit identification, ADMET prediction, and lead optimization.Conduct Structure-Activity Relationship (SAR) analysis, chemical space exploration, and property optimization using computational methods.Establish standards for compound library curation and high-quality assay data standardization for model training.2. Target and Systems Biology DiscoveryDrive novel target identification and validation using multi-omics data (genomics, proteomics, transcriptomics), network biology, and literature mining.Perform pathway analysis and deep disease biology interpretation to generate mechanism-level hypotheses for drug programs.Build scalable, production-ready workflows for biological data integration and quantitative target prioritization.3. Platform Integration and ML/AI ApplicationDesign modular, reproducible computational chemistry and biology workflows.Collaborate with software teams to productionize and scale pipelines on cloud/HPC.Integrate deep learning models with model tracking, versioning, and deployment.Support development and/or integration of agentic workflows into platforms.Contribute to scientific documentation and intellectual property (IP) generation.Required Technical SkillsStrong expertise in at least one core area of computational drug discovery:Ligand-Based Drug Design (LBDD) and/or Structure-Based Drug Design (SBDD)Computational Structural Biology (MD, FEP, docking, with proficiency in tools like GROMACS, AMBER, NAMD, and Desmond)Computational Systems Biology / Target DiscoveryHands-on experience with forcefield-based simulations.Direct exposure to implementing and utilizing deep learning in drug discovery, including generative models.Advanced proficiency in Python programming (RDKit, NumPy, PyTorch)Proven experience building and optimizing scalable computational scientific pipelines.Solid understanding of the end-to-end small molecule drug discovery decision process.Strong knowledge of best practices in benchmarking, reproducibility, and rigorous scientific validation.Preferred SkillsTrack record of contributions to research publications, patent applications, or invention disclosures.Educational QualificationsPhD in one of the following (mandatory):Computational ChemistryCheminformaticsComputational BiologyDrug DiscoverySystems BiologyRelated quantitative life sciences disciplineExperience Requirements2–5 years of relevant post-PhD experience in computational drug discovery/design and platform development or related fields.Demonstrated experience building end-to-end, high-throughput computational screening and analysis workflows.Experience working with complex, real-world biological and chemical datasets in a pharmaceutical or biotech context.Proven exposure to interdisciplinary collaboration.