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About the Role

We are seeking an experimental biologist to serve as a bridge between our AI models and the physical world. This hands-on role involves designing experiments, managing CROs, interpreting data, and collaborating with research teams to guide future development. The position also offers the opportunity to help build and operate internal wet-lab capabilities.

Responsibilities

Design experiments to test predictions, molecules, sequences, and biological systems generated or prioritized by our models.
Collaborate closely with ML researchers and computational biologists to translate modeling questions into fast, informative experiments.
Analyze experimental results, troubleshoot failures, and determine the highest-value next experiments.
Help shape scientific direction, including data generation, biological systems to pursue, and where experimental validation can unlock new capabilities.
Identify and manage CROs, academic labs, and other experimental partners across multiple programs.
Develop experimental plans, protocols, budgets, timelines, and success criteria, and work directly with partners to troubleshoot and adapt experiments.
Ensure experimental quality and that raw data, metadata, and documentation are suitable for downstream modeling.
Build a network of trusted partners capable of moving quickly across a broad range of biological systems.
Determine which experimental capabilities to outsource versus bring in-house, and build our internal wet-lab capabilities over time.
Design closed-loop workflows where models propose designs, experiments test them, and the resulting data improves the next generation of models.
Identify and generate high-value training and evaluation datasets that can unlock new model capabilities.
As the organization grows, help establish lab infrastructure, workflows, instrumentation, and eventually recruit and manage an internal experimental team.

Requirements

PhD or equivalent research experience in experimental biology, bioengineering, molecular biology, cancer biology, synthetic biology, or a related field.
Strong experimental instincts, with experience independently designing, executing, and troubleshooting rigorous wet-lab experiments.
Experience with high-throughput or pooled experimental systems such as CRISPR screens, MPRAs, or related approaches.
Experience managing CROs, core facilities, or academic collaborators, including evaluating protocols, controls, budgets, timelines, and data quality.
Able to critically interpret experimental results and communicate effectively with both experimental and computational researchers.
Comfortable working across biological domains and taking high ownership in a fast-moving research environment.
Excited to help build experimental capabilities and laboratory infrastructure from the ground up.

Skills

Experimental biology
Bioengineering
Molecular biology
Cancer biology
Synthetic biology
High-throughput experimental systems
Pooled experimental systems
CRISPR screens
MPRAs
CRO management
Core facility management
Academic collaboration
Experimental data interpretation
Communication with experimental and computational researchers
Functional genomics
Protein engineering
Virology
Therapeutic development
Generating experimental datasets for machine learning or AI
Building or scaling a wet lab
Managing research staff

Education Level

PhD or equivalent research experience

About the Company

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 gene therapy tool 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.
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.

Equal Opportunity

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.