Radical Numerics
radicalnumerics.ai
Actively hiring
Foundation Models
AI Research Labs
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News
Technologies
Salaries
Products
People
Growth
Offices
Jobs
Financials
Funding and backers
Team
201-500
employees, estimated range
Hiring now
16
+3 posted in 30 days
Backers
1
First Spark Ventures
Momentum
8/100
hiring for its size
Last sign of life
this month
a posting or a news item
The story so fareach stage dated by its source: the company's releases, the press, registers, filings
2025
Founded
Tractionpublic signals, read monthly
Web presence
top 2%
of 133.2M domains by links, Common Crawl
Hacker News stories
3
best: 4 points
Backed byfrom the funds' portfolios and accelerator catalogs
First Spark Ventures Investors in similar startupsfunds that back the companies most like this one
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How Radical Numerics hires
Truth index
A 95/100
Median time to fill
204 days
Ghost roles
0%
Stale roles
0%
Reposted roles
0%
Radical Numerics scores A on the Alion truth index (A is best, E worst), measured over 16 open roles re-checked against its own hiring board: 0% look like ghost listings, 0% have gone stale and 0% are the same role posted again. Its roles close in a median of 204 days. It measures how much of what this employer publishes turns into a real, finite search - not the employer as a workplace. How the index is computed
Overview
Radical Numerics is a research laboratory working on recursive self improvement of machine learning systems, where models help design the next generation of architectures. Its early releases included open diffusion language models trained through automated architecture search rather than hand designed recipes. The company was founded by researchers from leading industrial laboratories and publishes its methods alongside model weights; its research is released with open weights so that others can reproduce the search process.
News
A new frontier in generative genomics with Omnii
A research preview of Omnii, our next-generation genome language model.
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Report
Radical Numerics launches with $50M to build general biological intelligence
Radical Numerics launches with $50M to build general biological intelligence - SiliconANGLE
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Report
AI Lab Radical Numerics Launches with $50M Seed Round To Build General Biological Intelligence
SAN FRANCISCO, June 15, 2026--AI Lab Radical Numerics Launches with $50M Seed Round To Build General Biological Intelligence
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Report
AI Lab Radical Numerics Launches with $50M Seed Round To Build General Biological Intelligence
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Report
radicalnumerics/RND1-Base-0910 Hugging Face
We're on a journey to advance and democratize artificial intelligence through open source and open science.
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Report
RND1: simple, scalable AR-to-diffusion conversion
Introducing RND1-30B, the largest open-source diffusion language model, trained via AR-to-Diffusion conversion with a simple, scalable recipe.
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Report
Tech stack
Technologies named in job postings at Radical Numerics over the last 12 months, by layer, with the share of postings for each.
Technologies in use
35
Postings analysed
14
Stack modernity
61/100
Languages
Python79%
C++14%
Rust7%
TypeScript7%
Frameworks & libraries
PyTorch57%
Triton29%
CUDA21%
JAX14%
Cloud & platforms
CUDA Toolkit21%
vLLM14%
Kubernetes7%
Linux7%
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Full Radical Numerics tech stack
35 technologies · by team · pay by technology · changes · similar stacks
Growth
Hiring Momentum
61/100
Growing
Open positions
16
3 opened / 6 closed in 30 days
Median time-to-fill
138 days
faster than 10% of the market
ATS activity
Every ~4 hours
Today
Hiring Dynamics
+100%
Hiring Focus
The percentage next to each role is its share of the company's job openings over the last 90 days; the arrow shows the shift versus the previous period.
Product
30% ▼
AI/ML
10% ▼
HR
10% ▲
Management
10% ▲
Sales
10% ▼
Science & Research
10% ▼
Activity Timeline
Added Linux to stack
Sep 2026
Added Machine Learning to stack
Sep 2026
Added RLAIF to stack
Sep 2026
Added Reward Modeling to stack
Sep 2026
Added World Models to stack
Sep 2026
Added HIPAA to stack
Sep 2026
Added GDPR to stack
Sep 2026
Added TypeScript to stack
Sep 2026
Added Interpretability to stack
Sep 2026
Added Mixture of Experts to stack
Aug 2026
Added Human-in-the-Loop to stack
Aug 2026
Added Post-training to stack
Aug 2026
Offices
Where the company hires and what each office is for
Cities
2
Hiring now
2
Countries
2
Busiest office
San Francisco
Loading the map
Headquarters
Builds here
Office with open vacancies
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San Francisco
United States
HQ
16 open roles 10 posted in 90 days 50% engineering 200-500 est. staff
hires for AI/ML, Product, HR
Tokyo
Japan
Engineering / R&D
5 open roles 100% engineering 50-200 est. staff
hires for AI/ML
Jobs
≈ $173k – $317k per year (Estimated) • In office • Full-Time • San Francisco
Apply
≈ $173k – $317k per year (Estimated) • In office • Full-Time • San Francisco
Apply
≈ $184k – $376k per year (Estimated) • In office • Full-Time • San Francisco
Python
vLLM
TensorRT
TensorRT-LLM
Triton
Apply
≈ $197k – $403k per year (Estimated) • In office • Full-Time • San Francisco
Python
DeepSpeed
vLLM
CUDA Toolkit
Multimodal AI
SGLang
TensorRT
TensorRT-LLM
PyTorch
Mixture of Experts
CUDA
Triton
Apply
≈ $194k – $397k per year (Estimated) • In office • Full-Time • San Francisco • Tokyo
Python
Multimodal AI
PyTorch
Interpretability
Machine Learning
Apply
≈ $194k – $397k per year (Estimated) • In office • Full-Time • San Francisco • Tokyo
Python
Rust
C++
PyTorch C++
AI/ML
CUDA Toolkit
Multimodal AI
PyTorch
CUDA
Triton
Megatron-LM
NCCL
World Models
Machine Learning
DevOps
SLURM
Kubernetes
Linux
Apply
≈ $201k – $411k per year (Estimated) • In office • Full-Time • San Francisco • Tokyo
Python
Multimodal AI
PyTorch
Pre-training
World Models
Apply
≈ $204k – $417k per year (Estimated) • In office • Full-Time • San Francisco • Tokyo
Python
RLHF
Multimodal AI
PyTorch
Synthetic Data
Post-training
World Models
RLAIF
Reward Modeling
Apply
≈ $197k – $402k per year (Estimated) • In office • Full-Time • San Francisco • Tokyo
Python
C++
PyTorch C++
AI/ML
CUDA Toolkit
Multimodal AI
PyTorch
CUDA
Triton
Megatron-LM
World Models
Machine Learning
Apply
≈ $199k – $406k per year (Estimated) • In office • Full-Time • San Francisco
Python
Fine-tuning
JAX
Multimodal AI
Diffusion Models
PyTorch
Post-training
Interpretability
Machine Learning
Apply