Radical Numerics

Member of Technical Staff, Pretraining Science

San Francisco · Staff+

Work authorization required$26k-$42kDetected 127 days ago

PythonDistributed SystemsAlgorithmsMachine LearningDeep LearningPyTorchStatisticsA/B TestingBioinformaticsCRISPRResearchCommunication

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.
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.

Responsibilities

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.
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.
Ability to design, run, and analyze experiments thoughtfully, with strong research judgment and empirical rigor.
Design data curricula and sampling strategies.

Requirements

Experience working in distributed or high-performance computing environments.
Excellent written and verbal communication skills, especially the ability to explain complex technical findings clearly across engineering, research, and scientific collaborators.

Nice to have

Experience training or analyzing frontier or foundation models.
Strong grasp of probability, statistics, optimization, and ML fundamentals.
Experience designing or maintaining evaluation frameworks for large models.
Contributions to open-source ML systems, datasets, or research tooling.
Background in applied math, systems, computational biology, physics, mathematics, or another strongly quantitative field.
Work on fundamental questions in pretraining science while staying close to real scientific applications in biology.
Join a collaborative culture that values rigor, creativity, and cross-disciplinary partnership across AI labs, biotechs, hospital systems, and research institutes.
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

Competitive compensation, comprehensive benefits, and support for continual learning.

Benefits

Build and refine mixtures, curricula, and sampling policies that improve learning efficiency, generalization, and robustness across biological modalities and tasks.
Evaluate ideas in model design, optimization, long-context learning, and training stability that make large-scale biological pretraining more effective.
Strong track record in ML research or engineering, especially in large-scale model training, pretraining, representation learning, optimization, scaling laws, or related areas.
Proficiency in Python and modern deep learning tooling such as PyTorch, plus comfort debugging distributed or high-performance training systems at scale.

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.
Radical Numerics was founded to develop both the power to design and the responsibility to defend.

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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This listing is sourced directly from Radical Numerics's careers page and normalized into a canonical job model.