Zakres obowiązków
Responsibilities: Leadership & Management
• Cross-functional Collaboration: Partner closely with teams in medicinal chemistry, biology, and analytics to define synthesis plates and experiments, analyze results, and lead the iterative Design-Make-Test-Learn (DMTL) cycle,.
• Stakeholder Coordination: Orchestrate interactions across Molecure and external partners, including the consortium member, IChO PAN (Institute of Organic Chemistry Polish Academy of Sciences), spanning chemistry, biology, and Machine Learning (ML) disciplines.
• Platform Ownership: Evaluate and integrate external models and APIs, design human-in-the-loop tools for chemists, and own documentation and technology transfer processes.
• Scientific Leadership: Mentor a smaller Data Science (DS)/ML team, set rigorous engineering and scientific standards, lead code reviews, ensure experimental rigor, and contribute actively to intellectual property (IP) documentation, scientific publications, and regulatory-ready documentation.
Key Responsibilities: R&D
• Modeling: Design, train, and validate Deep Learning (DL)/ML models for de novo design (including diffusion, VAE, GAN, flow models, and genetic RL) and predictive tasks (affinity, selectivity, ADMET/PK/Tox, docking/ranking) for both protein and mRNA-binding targets,.
• Active Learning & RL: Develop and implement closed-loop systems that utilize biological readouts from the lab (activity and orthogonal assays) to dynamically update policy/reward models and prioritize the next batch of molecules for synthesis.
• Cheminformatics Stack: Maintain and develop RDKit pipelines, implement multi-objective scoring systems, and build robust filters against toxic structures (PAINS/reactive/SMARTS), synthetic accessibility issues, novelty metrics, and diversity measures.
• Structure/Sequence Modeling: Integrate conventional docking and scoring methods with ML surrogates; leverage advanced models, such as transformers and equivariant Graph Neural Networks (GNNs), for protein and RNA structure and sequence data; provide specialized support for RNA-targeted small-molecule modeling.
• Data & MLOps: Architect and maintain the data lake and feature store; ensure data governance and lineage (using tools like DVC/MLflow); oversee containerization (Docker/K8s) and Continuous Integration/Continuous Deployment (CI/CD) pipelines; and manage scalable training environments on cloud or High-Performance Computing (HPC) clusters to ensure reproducible science.
Wymagania
Minimum Qualifications
• PhD in Computer Science (CS)/ Statistics/Computational Chemistry/ Computational Biology/ Mathematics (or equivalent MSc coupled with 6–8 years of highly relevant industry experience)
• Minimum 5+ years of demonstrable experience building ML systems for drug discovery or bioinformatics with a history of production impact
• Expert proficiency in Python, DL frameworks (PyTorch/JAX/TensorFlow), GNNs for molecules, transformers for sequences, generative models (diffusion/VAE/GAN), and hands-on experience with Reinforcement Learning (RL) or Bayesian optimization for molecular design
• Expertise in cheminformatics (RDKit), Quantitative Structure-Activity/Property Relationships (QSAR/QSPR), feature engineering, and multi-task and uncertainty modeling
• Familiarity with molecular docking and scoring techniques, along with experience in property prediction (ADMET, phys-chem, developability)
• Proven MLOps skills, including experience with MLflow/DVC, Docker, Kubernetes (K8s), experiment tracking, data versioning, and CI/CD in cloud environments (AWS/Azure/GCP)
• Exceptional cross-disciplinary communication skills when collaborating with chemists and biologists, and fluent English language proficiency.
Nice to Have
• Specific experience with RNA-targeted small molecules, including RNA structure prediction and sequence-to-structure modeling
• Experience utilizing Large Language Models (LLMs) or AI agents for managing chemistry workflows, assay design, High-Throughput Screening (HTS), High-Content Screening (HCS), or Cell Painting methodologies
• Polish language proficiency and prior experience navigating Polish grant environments (such as NCBR/FENG) and collaborating with domestic industry partners.
Oferujemy
What We Offer
• A competitive salary package.
• Direct and real influence on the project's direction and long-term scientific and business vision.
• The opportunity to collaborate with top scientific talent and leading research institutes in Poland and internationally.