Member of Technical Staff, Mechanistic Interpretability

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 lower 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.

As a Member of Technical Staff, Mechanistic Interpretability at Radical Numerics, you will study how multimodal genome language models represent, process, and reason about information internally. Your work will focus on opening the black box: developing tools, experiments, and theories that help us understand model behavior, uncover internal mechanisms, and drive scientific discovery with real-world clinical applications. Interpretability doesn't just start and end at off-the-shelf models.

It's critical to our model understanding and model development itself, and pushes the boundaries of what these biological language models can do.

This is a research-oriented role for someone excited by the intersection of deep learning, scientific discovery, and model understanding. You should be motivated by questions such as:
What concepts emerge during training? Which circuits drive specific behaviors? How do we extract biological insights for understanding the fundamental pathways for disease? How do we control generative design for life saving treatments?

We believe that understanding frontier models will become increasingly important as they are applied to consequential domains in science and biology. Mechanistic interpretability offers a path toward deeper scientific insight into learning systems, improved model evaluations, and ultimately, mastery over the code of life. This role sits at the intersection of AI research, systems engineering, and scientific discovery.

Understand how frontier biological models work. Design and execute experiments to uncover the features, circuits, representations, and mechanisms that drive model behavior. Study how models learn, store, retrieve, and manipulate information across scales.

Build the tooling for model understanding. Develop infrastructure and methods for mechanistic interpretability, including activation analysis, causal interventions, probing, feature discovery, sparse representations, circuit tracing, and large-scale interpretability workflows.

Connect mechanisms to capabilities and failures. Investigate how internal representations relate to downstream performance, reasoning, robustness, cont rollability, and scientific usefulness. Identify the mechanisms underlying both desirable capabilities and failure modes.

Advance interpretability research. Develop new techniques for understanding large models and use them to improve evaluation, reliability, safety, and model design.

Study multimodal genome language models. Investigate how models represent biological concepts, mechanisms, and abstractions, and use those insights to better understand both the models themselves and the systems they are modeling.

Collaborate across research disciplines. Work closely with teams spanning model architecture, training, systems, safety, and biology to turn interpretability insights into better models, evaluations, and scientific…