Member of Technical Staff, Structure Prediction

About Us

Radical Numerics is an AI research lab building general biological intelligence. Our mission is to master the code of life, and our purpose is to reduce human suffering.

Our team created Evo, and started the field of generative genomics
. Our work was featured on the cover of Science, and presented by our CEO on the main stage of TED
2025. Evo was used to create the first AI gene therapy tool CRISPR-Cas9, and the first AI whole genome from scratch. Evo 2, featured in Nature, is the largest fully open source AI project across any domain.

Radical Numerics is bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We’ve redesigned the foundation model training stack to turn the world’s raw scientific data (e.g. biological sequences, experiments, and physical processes), into intelligible, generative models that can expand and accelerate what humanity can understand, design, and cure.

The same generative breakthroughs that enable life-saving cures also lowers the barrier to creating engineered threats and AI-generated bioweapons. We believe these forces are inseparable. Radical Numerics was founded to develop both the power to design and the responsibility to defend.

As a Member of Technical Staff, Protein Design, you will develop advanced machine-learning systems at the frontier of molecular modeling, from protein language models to structure prediction and beyond.

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.

• Develop and improve machine-learning models for protein structure prediction, design, 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.

• 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 and design methods, loss objectives, 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…