Scientist - Experimental Data Generation for AI/ML (Madison)

Our client is building the next generation of AI-driven structural biology, integrating cutting-edge in vivo data with machine learning to model protein conformations in disease. About the Role: We are seeking a highly motivated Scientist / Senior Scientist to lead experimental data generation for an AI/ML-driven structural biology platform. This role sits at the intersection of wet-lab science and machine learning, focused on generating high-quality, ML-ready datasets derived from complex biological systems.

You will play a critical role in designing and executing experiments that directly inform and improve machine learning models. This is a hands-on, cross-functional position working closely with computational teams to iterate rapidly and refine data quality and experimental approaches. Key Responsibilities: Own the wet-lab R&D pipeline for generating machine learning training data Translate AI/ML model requirements into well-designed experimental plans Design, execute, and analyze experiments end-to-end, including: Sample preparation and reagent selection Automation and liquid handling setup LC/MS operation and peptide mapping Data processing and interpretation Generate high-quality, structured datasets for machine learning applications Collaborate closely with computational teams to iterate experiments based on model feedback Maintain thorough documentation, data formatting, and dataset curation standards Source and manage biological reagents, inventory, and lab readiness Support external and internal projects by generating and analyzing experimental data Communicate findings and progress clearly to cross-functional stakeholder Required Qualifications: PhD (or MS with significant industry experience) in: Biochemistry Analytical Chemistry Chemical Biology Structural Biology Or related field Strong hands-on experience with LC/MS-based proteomics workflows Proven ability to independently design and execute complex experiments Experience working in fast-paced, evolving environments Strong communication and collaboration skills across scientific disciplines Excellent organizational and project management abilities Preferred Qualifications: Experience with structural mass spectrometry techniques, such as: Hydroxyl Radical Footprinting (HRF) HDX-MS, XL-MS, or related methods Familiarity with laboratory automation and liquid handling systems Proficiency in R or similar tools for data analysis and visualization Understanding of protein structure, conformational dynamics, or antibody systems Industry experience in drug discovery, biologics, or structural biology What You’ll Bring: A hands-on, problem-solving mindset with strong experimental rigor Ability to bridge experimental science and computational needs Curiosity and adaptability in a fast-moving, innovative environment A collaborative approach and passion for advancing scientific discovery Why Join: Opportunity to work on cutting-edge applications at the intersection of structural biology and AI/ML Direct impact on the development of novel therapeutic discovery platforms High level of ownership and influence on experimental strategy Collaborative, mission-driven environment focused on scientific innovation