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Location: SF Bay Area
Type: Full-time
Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast biological sequence, structure, and image datasets so that biological world models become a reality. Our team introduced hybrid architectures that unlocked million-token context windows, enabling work toward AI-designed whole genomes and real gene-editing tools.
We believe biology will be the most impactful and consequential application of AI—and that advancing capabilities must go hand-in-hand with advancing safety and biosecurity. The same systems that design biology must also help defend against it. This role is focused on building and deploying the technical systems that make biosecurity real.
As a Member of Technical Staff, Biosecurity at Radical Numerics, you will lead the design, evaluation, and deployment of biosecurity systems for biological foundation models. You will build evaluation frameworks, define safety architecture, and work with government and external partners to translate technical capabilities into real-world biosecurity infrastructure.
This role blends research and engineering. You should be excited to move fluidly between technical depth and external engagement: understanding model behavior, building red-teaming and evaluation pipelines, designing system-level safeguards, and working with stakeholders to deploy biosecurity systems at scale.
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Location: SF Bay Area
Type: Full-time
Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast multimodal biological datasets so that biological world models become a reality. Our team introduced the first hybrid architectures that unlocked million-token context windows, enabling the first AI-designed whole genomes and real gene-editing tools.
As a science-focused Member of Technical Staff, you will curate the multimodal biological datasets that power our models, analyze model behavior, and ensure our model outputs meet rigorous scientific standards. You'll co-develop benchmarks, filters, and validation pipelines with engineering peers so biological world models remain trustworthy and actionable.
Send your resume, a brief note on why Radical Numerics resonates with you, and examples of relevant public codebases you’ve built. We review applications on a rolling basis.
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Location: SF Bay Area or Tokyo, Japan
Type: Full-time
Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast biological sequence, structure, and image datasets so that biological world models become a reality. Our team introduced hybrid architectures for million-token context windows, enabling work toward AI-designed whole genomes and gene-editing tools.
We believe the next generation of biological foundation models will require not only better models and training systems, but also robust backend infrastructure that makes those systems usable in practice. This role focuses on the backend services and APIs that connect our research platform to internal tools, external products, and real-world scientific workflows.
As a Member of Technical Staff, Backend Engineering at Radical Numerics, you will design, build, and operate backend services that power APIs and platform capabilities across the company. You will help create the systems that make model capabilities accessible, reliable, and easy to integrate, whether for internal researchers, external users, or downstream scientific applications.
This is a hands-on role for someone who wants to own backend systems end-to-end. You should be excited to move from API design to implementation to deployment to observability, while working closely with researchers and product-minded engineers to ensure the systems we build are useful, scalable, and maintainable.
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Location: SF Bay Area
Type: Full-time
Radical Numerics is an AI lab bringing the rigor of distributed systems, bioinformatics, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast multimodal biological datasets so that biological world models become a reality. Our team introduced the first hybrid architectures that unlocked million-token context windows, enabling the first AI-designed whole genomes and real gene-editing tools.
We are seeking research scientists and engineers working at the intersection of machine learning and biological modeling to develop frontier AI architectures for biological problems.
In this role, you will extend and adapt large model backbones—such as sequence and multimodal foundation models—to enable tasks across genomics, protein biology, and cellular systems. This includes designing post-training pipelines, domain adaptation strategies, and evaluation frameworks that enable state-of-the-art ML frameworks to reason over biological data. You likely know the inner workings of frontier bio models such as AlphaFold, AlphaGenome, ESM, Evo, and thought about ways to improve, evaluate or apply them in novel ways.
You will collaborate with computational biologists to systems architecture researchers to translate advances in large-scale machine learning into capabilities for modeling biological systems, ranging from genome interpretation and regulatory modeling to multimodal cellular prediction and biological design.
Ready to apply?
Apply to Radical Numerics
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Location: SF Bay Area or Tokyo, Japan
Type: Full-time
Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast biological sequence, structure, and image datasets so that biological world models become a reality. Our team introduced hybrid architectures for million-token context windows, enabling work toward AI-designed whole genomes and gene-editing tools.
We believe the next generation of biological foundation models will require not only new and improved pretraining recipes, but also innovation on post-training: the work that turns a powerful base model into a system that is useful, steerable, robust, and scientifically productive. This role sits at that interface between fundamental research and practical engineering.
As a Member of Technical Staff, Post-Training at Radical Numerics, you will develop the training and evaluation loops that shape biological world models after pretraining. You will work on the methods, data, and infrastructure required to improve model behavior on real scientific tasks: reasoning over long biological context, following complex objectives, making useful predictions, and interacting reliably with downstream tools and workflows.
This is a hands-on role for someone who wants to both build systems and deepen understanding. You should be excited to run careful experiments, question whether the metrics reflect reality, and translate empirical findings into better recipes, datasets, and productively used models.
Ready to apply?
Apply to Radical Numerics
Share this job
Location: SF Bay Area or Tokyo, Japan
Type: Full-time
Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast biological sequence, structure, and image datasets so that biological world models become a reality. Our team introduced hybrid architectures that unlocked million-token context windows, enabling work toward AI-designed whole genomes and real gene-editing tools.
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.
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.
Why Radical Numerics
Ready to apply?
Apply to Radical Numerics
Share this job
Location: SF Bay Area or Tokyo, Japan
Type: Full-time
Radical Numerics is an AI lab bringing the rigor of distributed systems, model architecture, and numerics research to the challenges of biology. We are building the infrastructure needed to unlock scaling on vast biological sequence, structure, and image datasets so that biological world models become a reality. Our team introduced hybrid architectures that unlocked million-token context windows, enabling work toward AI-designed whole genomes and real gene-editing tools.
We believe biological world models will require not only strong research ideas, but exceptional training and inference systems: infrastructure that makes large-scale experimentation efficient, reproducible, and robust enough to support rapid scientific iteration. This role is focused on building that foundation.
As a Member of Technical Staff, Infrastructure & Training Systems at Radical Numerics, you will design and build the systems that make large-scale model training possible across research and deployment workflows. You will work on distributed training, performance optimization, reusable internal frameworks, and the tooling that helps researchers move quickly without sacrificing reliability.
This role is ideal for someone who combines deep systems instincts with an interest in modern machine learning. You should care about how every layer of the stack affects research velocity: kernel performance, communication overhead, fault tolerance, observability, reproducibility, and the ergonomics of the training loop itself.
Ready to apply?
Apply to Radical Numerics
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