---
title: 'Member of Technical Staff, Pretraining Science at Radical Numerics'
canonical: 'https://feeny.ai/job/member-of-technical-staff-pretraining-science-radical-numerics-san-francisco-535axv583h96'
type: 'job'
last_seen: '2026-09-07'
---

# Member of Technical Staff, Pretraining Science at Radical Numerics

- **Company:** Radical Numerics
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-03-17
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/radical-numerics/0b3b12c1-c8ca-492b-9770-969df46004de

## Job description

## ABOUT US

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.

## 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

- Strong track record in ML research or engineering, especially in large-scale model training, pretraining, representation learning, optimization, scaling laws, or related areas.
- Ability to design, run, and analyze experiments thoughtfully, with strong research judgment and empirical rigor.
- Proficiency in Python and modern deep learning tooling such as PyTorch, plus comfort debugging distributed or high-performance training systems at scale.
- 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.
- Intellectual curiosity and a bias toward experimentation, iteration, and continuous improvement.

## NICE TO HAVE

- Experience training or analyzing frontier or foundation models.
- Strong grasp of probability, statistics, optimization, and ML fundamentals.
- Familiarity with curriculum learning, data selection, active learning, or data-quality methods for large-scale training.
- 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.

## WHY RADICAL NUMERICS

- Help build the multimodal biological world models needed for rapid detection, response, and countermeasures across global health.
- 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.
- Competitive compensation, comprehensive benefits, and support for continual 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.

## About Radical Numerics

## Company Overview
- **One-liner**: Radical Numerics is a next-generation AI lab building general biological intelligence, developing multimodal models trained on the fabric of biology to enable discovery, design, and defense in the life sciences.
- **Entity Type**: Private (Seed stage)
- **Headquarters**: San Francisco, California, USA (with a presence in Tokyo, Japan)
- **Founded**: Not publicly available (recently announced a $50M seed round)
- **Founders**: Not publicly listed (the founding team previously trained Evo and Evo 2, the largest biological AI models trained on DNA)

## Core Business
- **Primary industry**: Artificial Intelligence / Biotechnology (AI for Biology)
- **Target customers**: B2B — partnerships with diagnostics companies, U.S. national labs, and pharmaceutical/therapeutic organizations; also serves the broader scientific community through open-source models.
- **Mission or purpose statement**: "To master the code of life and to ensure this power is used to save lives." The company is building general biological intelligence to enable faster discovery, deeper understanding, and entirely new capabilities in biology, while simultaneously building biodefense systems to safeguard against misuse.

## Products & Services
- **Evo & Evo 2**: The founding team's prior open-source generative genomics models, trained on millions of genomes across all of life. Evo was used to generate the world's first complete genome from scratch using AI (a bacteriophage). These models established the field of generative genomics.
- **Omnii**: The company's next-generation genome language model (gLM). It features a multi-hybrid architecture with a 2M-token context window, multimodality, and is the first aligned large-scale genome model. It surpasses specialized models across core genetics benchmarks, especially in noncoding and regulatory regions. Omnii is being piloted for early cancer detection (with a diagnostics partner) and for biodefense/biosurveillance (with a U.S. national lab).
- **Biodefense Platform (Omnii for Biosecurity)**: A system designed to detect "deepfake" pathogens, identify suspicious sequences, and attribute engineered function — aimed at global biological resilience against natural, synthetic, and AI-generated threats.

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private company)
- **Key Metric**: $50 million seed round (announced in 2025)
- **Notable Investors/Partners**: Not publicly named in the seed round announcement. Advisors include Eric Horvitz (CSO of Microsoft), Chris Ré (Stanford), George Church (Harvard), and Andrew Weber (former Assistant Secretary of Defense for Nuclear, Chemical and Biological Defense Programs). Partners include a U.S. national lab (for Omnii biodefense pilot) and a diagnostics company (for early cancer detection).
- **Growth Signals**: Raised a $50M seed round; has a data center under construction filled with NVIDIA Blackwells; actively hiring for 11+ roles across San Francisco and Tokyo; building the next generation of biological AI models with a 2M-token context window; partnering with a U.S. national lab and a diagnostics company.

## Competitive Advantages
- **Proven Track Record**: The founding team created Evo and Evo 2, the largest open-source biological AI models, which established the field of generative genomics. Evo generated the world's first complete AI-designed genome.
- **First-Mover in Alignment for Biology**: Omnii is the first large-scale genome model to incorporate alignment methods (mid- and post-training), transforming a language model into a reliable scientific instrument.
- **Dual Mandate (Design + Defense)**: Uniquely positioned as both a design lab and a defense lab under one roof, addressing the inseparable forces of biological advancement and biosecurity.
- **World-Class Advisors**: Eric Horvitz (Microsoft CSO), Chris Ré (Stanford), George Church (Harvard), and Andrew Weber (former Assistant Secretary of Defense).
- **Technical Depth**: Team has pioneered models with one-million-token context windows and is now scaling toward one billion, with a focus on novel architectures and systems engineering for scientific data.

## Strategic Focus
- **Scaling Biological AI**: Building the infrastructure (data center with NVIDIA Blackwells) to train the most powerful biological AI models in the world.
- **Dual Application**: Simultaneously advancing human health (cancer detection, therapeutic design, variant interpretation) and biodefense (pathogen detection, biosurveillance with U.S. national labs).
- **Model Innovation**: Developing new multi-hybrid architectures, long-context windows (2M tokens and scaling to 1B), multimodality, and mechanistic interpretability tools for biology.
- **Open Science Foundation**: While building proprietary systems, the team's prior work (Evo, Evo 2) was released fully open source, establishing credibility in the scientific community.

## Why Work Here
- **Mission-Driven**: The company is tackling "the most important scientific and technological challenge of our time" — mastering the code of life to cure disease, engineer new medicines, and defend against biological threats.
- **Cutting-Edge Technical Work**: Engineers and researchers work on frontier AI challenges including distributed systems, model architecture, numerics research, long-context models, and mechanistic interpretability — all applied to biological data.
- **On-Site Culture**: The vast majority of roles (10 of 11 listed) are on-site in San Francisco, with some roles also available in Tokyo. This suggests a strong in-person collaboration culture.
- **Interdisciplinary Team**: Brings together AI researchers, systems engineers, computational biologists, and scientists from institutions including Stanford, MIT, and Google DeepMind.
- **High Ambition, Early Stage**: With a $50M seed round and a small team, new hires have the opportunity to shape the company's technical direction and infrastructure from the ground up.
- **Dual Focus on Impact and Responsibility**: The company explicitly states that "the design lab and the defense lab need to be the same entity," offering engineers a chance to work on both creation and safeguarding of powerful technology.

## Sources
1. [radicalnumerics.ai](https://www.radicalnumerics.ai/)
2. [radicalnumerics.ai/about](https://www.radicalnumerics.ai/about)
3. [radicalnumerics.ai/blog](https://www.radicalnumerics.ai/blog/radical-numerics-seed)
4. [radicalnumerics.ai/join-us](https://www.radicalnumerics.ai/join-us)
5. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/radical-numerics)

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