---
title: 'Member of Technical Staff, Protein Design at Radical Numerics'
canonical: 'https://feeny.ai/job/member-of-technical-staff-protein-design-radical-numerics-san-francisco-qb0q6hcghd3c'
type: 'job'
last_seen: '2026-09-07'
---

# Member of Technical Staff, Protein Design at Radical Numerics

- **Company:** Radical Numerics
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Posted:** 2026-07-22
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/radical-numerics/42956dc6-1ebd-4fbb-bcb7-974726a859bb

## 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, Protein Design, you will develop advanced machine-learning systems at the frontier of molecular modeling,from protein language models to structure prediction and beyond.

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, design, 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 and design methods, loss objectives, 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.
- Familiarity with multiple sequence alignments, templates, coevolutionary methods, inverse folding, molecular simulation, or energy-based modeling.
- Experience modeling other macromolecules, small molecule systems, and molecular interactions.
- Understanding of over major protein structure datasets, benchmarks, or community evaluation efforts.
- Experience modeling protein complexes or alternative conformational states.
- 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 generative modeling, molecular design, 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.

## 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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