We're growing rapidly at @RadicalNumerics and scaling our core teams. Join us in building the next generation of scientific world models.
We're hiring across a few roles, each with significant ownership and cross-functional scope:
- Member of Technical Staff, Post-Training
- Member of Technical Staff, Infrastructure and Training Systems
- Member of Technical Staff, Pretraining Science
- Member of Technical Staff, AI Bio
- Member of Technical Staff, Biosecurity
Our technology brings together numerics, systems engineering, and architecture design to tackle large-scale pretraining on scientific data. Our blogs (see below) give a flavor of the work.
We believe 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.
Ping me or others in the team if you'd like to learn more.
https://t.co/LaC63aXZ5l
We're growing rapidly at @RadicalNumerics and scaling our core teams. Join us in building the next generation of scientific world models.
We're hiring across a few roles, each with significant ownership and cross-functional scope:
- Member of Technical Staff, Post-Training
- Member of Technical Staff, Infrastructure and Training Systems
- Member of Technical Staff, Pretraining Science
- Member of Technical Staff, AI Bio
- Member of Technical Staff, Biosecurity
Our technology brings together numerics, systems engineering, and architecture design to tackle large-scale pretraining on scientific data. Our blogs (see below) give a flavor of the work.
We believe 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.
Ping me or others in the team if you'd like to learn more.
https://t.co/LaC63aXZ5l
We’re growing fast at @RadicalNumerics and expanding the core technical teams building our scientific world models.
That means new architectures, new training systems, and new ways of scaling across massive multimodal datasets.
We’re hiring in Infrastructure & Training Systems, Pretraining Science, Post-Training, AI for Biology, and Biosecurity.
Each role has significant ownership and a cross-functional scope.
Ping us if you’d like to learn more!
https://t.co/oFAYX5J871
To get the most out of your AI and infrastructure, optimizations are critical.
@RadicalNumerics breaks down our NVFP4 pretraining recipe, covers why NVFP4 matters, and calls out the 4 techniques for delivering numerically stable NVFP4 training on #NVIDIABlackwell.
Learn how 4-bit precision is enabling LLM pretraining more efficiently. https://t.co/BaXaMz1g4t
Scaling scientific world models requires co-designing architectures, training objectives, and numerics. Today, we share the first posts in our series on low-precision pretraining, starting with NVIDIA's NVFP4 recipe for stable 4-bit training.
Part 1: https://t.co/3uy4r1g6qk
Part 2: https://t.co/w6zPcoNsD9
We cover floating point fundamentals, heuristics, custom CUDA kernels, and stabilization techniques. Future entries will cover custom recipes and results on hybrid architectures.
Diffusion is poised to overtake autoregression in language modeling: parallel, order-flexible generation makes inference faster and more steerable. With RND1, we’re taking a step: the largest open diffusion LM. Sparse MoE (30B, 3B active). Releasing the model, training recipe, and inference code:
Introducing RND1, the most powerful base diffusion language model (DLM) to date.
RND1 (Radical Numerics Diffusion) is an experimental DLM with 30B params (3B active) with a sparse MoE architecture.
We are making it open source, releasing weights, training details, and code to catalyze further research on DLM inference and post-training.
We are researchers and engineers (DeepMind, Meta, Liquid, Stanford) building the engine for recursive self-improvement (RSI) — and using it to accelerate our own work. Our goal is to let AI design AI.
We are hiring.
Grafting Diffusion Transformers accepted to #NeurIPS2025 as an Oral! We have lots of interesting analysis, a test bed for model grafting, and insights🚀
📄Paper: https://t.co/9Horic5YTo
🌎Website: https://t.co/xZomk7Vq6e
In April '25, I shared the origin story of Evo on the TED stage.
I talked about the motivation behind generating DNA with AI and how it could change what’s possible.
It was an incredible experience.
full video: https://t.co/tstT6cQI6u
Also: Excited that this coincides with the release of the @TEDTalks of our Co-Founder @exnx on how AI could generate new life forms: https://t.co/XGF137xuJh
Update: I co-founded @RadicalNumerics with @MichaelPoli6, @Massastrello , @exnx, and a stellar team.
AI is changing everything — except itself. We’re building the engine for recursive self-improvement: AI that designs and refines AI, accelerating discovery in science+industry.
Life update: I started Radical Numerics with Stefano Massaroli, Armin Thomas, Eric Nguyen, and a fantastic team of engineers and researchers. We are building the engine for recursive self‑improvement (RSI): AI that designs and refines AI, accelerating discovery across science and industry.
Three core beliefs:
- We need orders of magnitude more AI systems in the world: models and interfaces built with purpose, to augment specific domains and workflows
- The complexity and resources required to develop AI are growing rapidly. Human development speed is the bottleneck.
- The process of developing frontier AI in new domains is ripe for disruption.
https://t.co/I1HNkCF1Ko
✨ Excited to share a few life updates!
🎤 My TED Talk is now live! I shared the origin story of Evo, titled: "How AI could generate new life forms"
TED talk: https://t.co/vi6m3DXkLV
✍️ I wrote a blog post about what it’s *really* like to deliver a TED talk
blog:
https://t.co/IuR5OAAdLT
🚀 And… I co-founded a new company called @RadicalNumerics - AI that designs itself using recursive self-improvement (RSI). Evo was just the start… we want many more Evos in bio and *beyond*
website: https://t.co/QEvYthZfa6
@TEDTalks@RadicalNumerics@MichaelPoli6 @ai_with_brains @Massastrello