Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Strong Tier-1 employer but niche protein-structure ML reduces applicant density.
Highly domain-specific antibody and protein-structure ML skills limit cross-industry transferability.
Requires deep protein-structure ML expertise, specific frameworks, and wet-lab collaboration.
Develop and validate machine learning models to predict protein function from structure, focusing on antibodies and antibody-like molecules.
Design and maintain validation benchmarks for antibody binding models using internal and external datasets to support model selection and quality control.
Partner with wet-lab scientists to guide experimental data generation and integrate protein structure prediction, generation, and inverse-folding tools into reproducible protein design workflows.
Bachelor’s degree with 6+ years relevant experience, OR Master’s degree with 4+ years relevant experience, OR PhD in relevant quantitative or life science field.
Proficiency in Python and experience with modern deep learning frameworks such as PyTorch or JAX.
Strong hands-on experience in protein structure modeling, especially related to antibodies.
Experience applying deep learning methods to protein structural data, including graph neural networks or equivariant models.
Has combined expertise in machine learning and protein structure modeling related to biologics discovery and antibody engineering.
Experienced in developing and adapting ML models and designing validation strategies for real-world discovery impact.
Comfortable collaborating across disciplines including computational biology, structural biology, protein engineering, and experimental teams to integrate ML workflows into biologics design pipelines.