Radical Numerics

Radical Numerics

Member of Technical Staff, Structure Prediction

San Francisco · Staff+

Sponsorship not specified$26k-$42kDetected 13 hours ago
PythonDistributed SystemsMachine LearningDeep LearningPyTorchData EngineeringA/B TestingBioinformaticsCRISPRResearchCommunication

About the role

  • You will work at the intersection of large-scale biological models, geometric deep learning, and structural biology.
  • This is a hands-on research and engineering role.

Responsibilities

  • Build reliable data pipelines and evaluation systems for structural modeling.
  • Design rigorous benchmarks that measure generalization and minimize data leakage or memorization.
  • Collaborate with scientists and engineers to translate research advances into robust modeling capabilities.

Requirements

  • 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.
  • Familiarity with commonly used protein structure metrics and evaluation practices.
  • 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.

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.
  • 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.

Compensation

  • $26k-$42k

Benefits

  • 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.
  • 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.

Company info

  • Radical Numerics http://radicalnumerics.ai 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 https://www.science.org/doi/10.1126/science.ado9336, and presented by our CEO on the main stage of TED2025 https://www.youtube.com/watch?v=EnbfoFUFm2s. Evo was used to create the first AI gene therapy tool CRISPR-Cas9, and the first AI whole genome https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1 from scratch. Evo 2 https://www.nature.com/articles/s41586-026-10176-5, 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.
  • Radical Numerics http://radicalnumerics.ai 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 https://www.science.org/doi/10.1126/science.ado9336, and presented by our CEO on the main stage of TED2025 https://www.youtube.com/watch?v=EnbfoFUFm2s.
  • Evo was used to create the first AI gene therapy tool CRISPR-Cas9, and the first AI whole genome https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1 from scratch.
  • Evo 2 https://www.nature.com/articles/s41586-026-10176-5, 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.

Visa & Work Authorization

  • 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.
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