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
