Member of Technical Staff, Experimental Biology

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 TED
2025. 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.

About the Role

We're hiring an experimental biologist to help build the bridge between our AI models and the physical world.

This is a highly hands-on operational role. You'll design experiments to test ideas generated by our models, identify and manage the CROs and external laboratories that can execute them, interpret the resulting data, and work directly with our research teams to decide what to build next.

This is not a traditional CRO operations role. We are looking for someone who can move fluidly between scientific strategy and execution: understanding what question we're trying to answer, designing the right experiment, getting it run quickly and rigorously, interpreting what happened, and turning the result into the next modeling or experimental decision.

Secondarily, this person will have the opportunity to help design, build, and ultimately operate Radical Numerics' internal wet-lab capabilities.

• Design experiments to test predictions, molecules, sequences, and biological systems generated or prioritized by our models

• Work closely with ML researchers and computational biologists to turn modeling questions into fast, informative experiments

• Analyze experimental results, troubleshoot failures, and determine the highest-value next experiments

• Help shape scientific direction, including what data to generate, which 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

• Help 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

• 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…