About the RoleAs a Member of Technical Staff focused on Protein Structure Modeling, you will develop advanced machine-learning systems for understanding and predicting protein structure.
You will work at the intersection of large-scale biological models, geometric deep learning, and structural biology. The role spans model development, training, evaluation, and scientific analysis, with a strong emphasis on building systems that generalize beyond standard benchmarks.
This is a hands-on research and engineering role. You will be expected to implement models, run large-scale experiments, diagnose failure modes, and develop rigorous ways to evaluate scientific performance. You will collaborate closely with researchers across machine learning, computational biology, and biological modeling.
What You'll Do- Develop and improve machine-learning models for protein structure prediction and related structural biology tasks.
- Train and fine-tune protein language models, geometric neural networks, diffusion models, and other modern scientific machine-learning architectures.
- Explore new architectures and learning objectives for modeling protein sequence and structure.
- Build reliable data pipelines and evaluation systems for structural modeling.
- Design rigorous benchmarks that measure generalization and minimize data leakage or memorization.
- Evaluate models using established structural accuracy, confidence, and physical-validity metrics.
- Analyze model performance across diverse proteins, structural classes, and biological contexts.
- Run ablation studies and controlled experiments to understand the impact of model architecture, data, scale, and training methodology.
- Improve the efficiency and reliability of model training and inference on large-scale compute systems.
- Collaborate with scientists and engineers to translate research advances into robust modeling capabilities.
What We're Looking For- Strong experience developing machine-learning models for protein structure prediction, structural biology, geometric deep learning, or a closely related area.
- Experience training or fine-tuning protein language models, structure models, diffusion models, or other large scientific machine-learning systems.
- Deep understanding of modern protein structure-prediction methods and architectures.
- Familiarity with geometric neural networks, equivariant architectures, pairwise representations, and generative modeling of molecular structure.
- Strong knowledge of protein structure, including secondary and tertiary structure, protein domains, complexes, conformational flexibility, and evolutionary constraints.
- Experience building and validating biological datasets and controlling for data leakage, homology, and benchmark contamination.
- Familiarity with commonly used protein structure metrics and evaluation practices.
- Fluency in Python and a modern deep-learning framework such as PyTorch or JAX.
- Experience with distributed training, accelerators, large datasets, and reproducible experimentation.
- Strong experimental judgment and the ability to distinguish genuine scientific progress from benchmark artifacts.
- Ability to independently move between research, implementation, experimentation, and scientific analysis.
- Clear communication skills and an ability to collaborate across machine learning, computational biology, and engineering.
- Expertise in machine learning, computational biology, structural biology, biophysics, computer science, or a related field, or an equivalent record of research and engineering impact.
Nice to Have- Contributions to protein structure-prediction systems, protein foundation models, geometric generative models, or widely used structural biology software.
- Experience with major protein structure datasets, benchmarks, or community evaluation efforts.
- Experience modeling protein complexes, molecular interactions, or alternative conformational states.
- Familiarity with multiple sequence alignments, templates, coevolutionary methods, inverse folding, molecular simulation, or energy-based modeling.
- Experience with SE(3)- or E(3)-equivariant architectures, diffusion models, flow matching, or generative modeling of molecular coordinates.
- Experience evaluating model confidence, uncertainty, and calibration.
- Familiarity with experimental methods for determining protein structure.
- A record of publications, open-source contributions, or production systems demonstrating impact in protein modeling or scientific machine learning.
