Member of Technical Staff, Protein Design

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 TED2025. 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. About the Role As 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. Radical Numerics is committed to equal employment opportunity and does not discriminate in any employment opportunities or practices based on an individual's race, color, creed, gender (including gender identity and gender expression), religion (all aspects of religious beliefs, observance or practice, including religious dress or grooming practices), marital status, registered domestic partner status, age, national origin or ancestry (including language use restrictions and possession of a driver’s license issued under California Vehicle Code section 12801.9), natural hair, physical or mental disability, political affiliation, medical condition (including cancer or a record or history of cancer, and genetic characteristics), sex (including pregnancy, childbirth, breastfeeding or related medical condition), genetic information, sexual orientation, military and veteran status or any other consideration made unlawful by federal, state, or local laws. It also prohibits unlawful discrimination based on the perception that anyone has any of those characteristics, or is associated with a person who has or is perceived as having any of those characteristics. Radical Numerics participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S. #J-18808-Ljbffr Radical Numerics