Member of Technical Staff, Pretraining Science

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-generated 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.

In October, we introduced Omnii-1, the world's most advanced multimodal genome language model, and a leap toward general biological intelligence. It unifies 28 biological modalities, spanning DNA, RNA, proteins, epigenomics, and more, in a single model trained on trillions of tokens of biological data In a world first, Omnii-1 designed the first AI-generated bacteriophages to be administered to a human patient with an antibiotic-resistant infection under compassionate use. We released this news alongside seven other project areas, showing the model’s breadth of applications.

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, Pre-Training Science at Radical Numerics, you will work on the science of how biological world models learn during large-scale training. You will develop new pretraining methods, study scaling behavior, and design training recipes that improve efficiency, generalization, and downstream scientific usefulness.

This role blends research and engineering. You should be excited to move fluidly between theory and implementation: reading technical literature, proposing new hypotheses, running large-scale experiments, and writing high-performance code that turns ideas into measurable progress.

We believe that biological foundation models will require advances not only in systems and scale, but also in the science of pretraining itself: how models learn from diverse biological data, what objectives produce useful representations, and how training recipes evolve as models and datasets grow. This role is focused on that core scientific agenda.

What You’ll Do

Research and develop new pretraining methodologies. Explore how biological world models learn from multi-modal data (eg, sequence, structure, and image data), and develop new objectives, training strategies, or architectural ideas that improve representation quality and downstream performance.

Study scaling behavior. Investigate how training dynamics change with model size, data composition, context length, and compute budget. Use empirical results to inform scaling protocols and future research priorities.

Design data curricula and sampling strategies. Build and refine mixtures, curricula, and sampling policies that improve learning efficiency, generalization, and robustness across biological modalities and tasks.

Work on architecture, algorithms, and optimization. Evaluate ideas in model design, optimization, long-context learning, and training stability that make large-scale biological pretraining more effective.

Run large-scale experiments rigorously. Design, execute, and analyze experiments with strong empirical discipline. Distinguish real effects from bugs, noise, or benchmark artifacts, and convert findings into better training recipes.

Collaborate closely with infrastructure and data teams. Work across the stack to ensure large-scale experiments are reproducible, efficient, and instrumented well enough to support fast scientific iteration.

Define evaluations for pretraining progress. Build and improve evaluation suites that measure representation quality, long-context behavior, transfer to downstream biological tasks, and scientific utility.

What We’re Looking For