Member of Technical Staff, AI Bio
- Compensation
- Not disclosed
- Location
- San Francisco
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Software engineering
- Posted
Skills
Must have
- Strong background and intuition in machine learning and deep learning across large-scale generative architectures, from autoregressive LLMs to diffusion models.
- Experience adapting large models to new domains through fine-tuning, post-training, adapters, or architecture modifications.
- Experience applying ML models to biological data and challenging prediction tasks.
- Familiarity with molecular biology and biological data modalities, particularly genomics, gene regulation, protein biology, or cellular systems.
- Ability to design evaluation tasks and benchmarks that measure biological model capability beyond simple accuracy metrics, with a critical eye toward aligning computational outputs with actionable dow
- Strong Python and ML tooling experience (PyTorch or JAX, experiment management, distributed training).
- Ability to interpret biological datasets and translate biological questions into machine learning experiments.
- Mid-to-senior level experience building and deploying ML systems in research or production environments.
Nice to have
- Experience with genomic or molecular sequence models (e.g., Evo, HyenaDNA, AlphaFold-style models, AlphaGenome-style tasks, virtual cell models).
- Background in ML for structural biology, or (bio)chemistry.
- Familiarity with multimodal biological modeling, including transcriptomics, epigenomics, chromatin accessibility, or spatial biology.
- Experience building robust evaluation suites for large AI systems.
- Experience scaling ML experiments across large GPU clusters.
- Research publications in ML for biology, chemistry, or related areas.
Full description
About Us
Radical Numerics 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, and presented by our CEO on the main stage of TED2025. Evo was used to create the first AI-generated CRISPR-Cas9, and the first AI whole genome from scratch. Evo 2, featured in Nature, is the largest fully open source AI project across any domain.
In October, we introduced Omnii-1, the world's most advanced multimodal genome language model, and a leap toward general biological intelligence. It unifies 28 biological modalities, spanning DNA, RNA, proteins, epigenomics, and more, in a single model trained on trillions of tokens of biological data In a world first, Omnii-1 designed the first AI-generated bacteriophages to be administered to a human patient with an antibiotic-resistant infection under compassionate use. We released this news alongside seven other project areas, showing the model’s breadth of applications.
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
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 designingpost-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.
What You'll Do
- Adapt frontier AI models to biological tasks through fine-tuning, post-training, adapters, and architectural modifications.
- Design and run experiments applying large models to problems in genomics, regulatory biology, protein biology, or cellular systems.