Scientist - Experimental Data Generation for AI/ML

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 dataTranslate AI/ML model requirements into well-designed experimental plansDesign, execute, and analyze experiments end-to-end, including:Sample preparation and reagent selectionAutomation and liquid handling setupLC/MS operation and peptide mappingData processing and interpretationGenerate high-quality, structured datasets for machine learning applicationsCollaborate closely with computational teams to iterate experiments based on model feedbackMaintain thorough documentation, data formatting, and dataset curation standardsSource and manage biological reagents, inventory, and lab readinessSupport external and internal projects by generating and analyzing experimental dataCommunicate findings and progress clearly to cross-functional stakeholderRequired Qualifications:PhD (or MS with significant industry experience) in:BiochemistryAnalytical ChemistryChemical BiologyStructural BiologyOr related fieldStrong hands-on experience with LC/MS-based proteomics workflowsProven ability to independently design and execute complex experimentsExperience working in fast-paced, evolving environmentsStrong communication and collaboration skills across scientific disciplinesExcellent organizational and project management abilitiesPreferred Qualifications:Experience with structural mass spectrometry techniques, such as:Hydroxyl Radical Footprinting (HRF)HDX-MS, XL-MS, or related methodsFamiliarity with laboratory automation and liquid handling systemsProficiency in R or similar tools for data analysis and visualizationUnderstanding of protein structure, conformational dynamics, or antibody systemsIndustry experience in drug discovery, biologics, or structural biologyWhat You’ll Bring:A hands-on, problem-solving mindset with strong experimental rigorAbility to bridge experimental science and computational needsCuriosity and adaptability in a fast-moving, innovative environmentA collaborative approach and passion for advancing scientific discoveryWhy Join:Opportunity to work on cutting-edge applications at the intersection of structural biology and AI/MLDirect impact on the development of novel therapeutic discovery platformsHigh level of ownership and influence on experimental strategyCollaborative, mission-driven environment focused on scientific innovation