Scientist - Experimental Data Generation AI​/ML; Structural Proteomics

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.

• 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 stakeholders

• 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

• Experience with structural mass spectrometry techniques, such as:
  • Hydroxyl Radical Foot printing (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

• 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

• 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