Radical Numerics

Radical Numerics

Member of Technical Staff, Mechanistic Interpretability

San Francisco · Staff+

Work authorization required$26k-$42kDetected 37 days ago
PythonDistributed SystemsMachine LearningDeep LearningPyTorchNLPLLMsSystems EngineeringBioinformaticsCRISPRResearchCommunication

About the role

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

Responsibilities

  • 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.
  • Advance interpretability research. Develop new techniques for understanding large models and use them to improve evaluation, reliability, safety, and model design.
  • 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 outcomes.
  • Curiosity about how models work internally and a desire to develop deeper scientific understanding rather than treating models as black boxes.
  • Design and execute experiments to uncover the features, circuits, representations, and mechanisms that drive model behavior.

Requirements

  • Proficiency in Python and PyTorch, with experience building research tooling and conducting rigorous empirical investigations.
  • You are comfortable designing studies, evaluating competing hypotheses, and distinguishing meaningful findings from artifacts.
  • Excellent written and verbal communication skills, including the ability to clearly explain technical insights and research results.

Nice to have

  • Experience with mechanistic interpretability techniques such as activation patching, causal tracing, probing, sparse autoencoders, feature analysis, circuit discovery, or representation analysis.
  • Research experience in frontier AI systems, AI safety, alignment, or model evaluations.
  • Experience working with large-scale training systems, distributed computing, or model infrastructure.
  • Background in computational biology, genomics, neuroscience, complex systems, or another scientific field involving high-dimensional data.
  • Contributions to open-source ML research, interpretability tooling, or model analysis frameworks.
  • At Radical Numerics, interpretability is not an afterthought.
  • 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.

Skills

  • Interpretability doesn't just start and end at off-the-shelf models.
  • You should be motivated by questions such as:
  • What concepts emerge during training?
  • Which circuits drive specific behaviors?

Compensation

  • $26k-$42k

Benefits

  • Strong background in machine learning research, particularly in large language models, representation learning, mechanistic interpretability, model analysis, AI safety, or related areas.
  • Deep understanding of modern deep learning architectures and training methods, including transformers, representation learning, optimization, and large-scale model systems.

Company info

  • Radical Numerics http://radicalnumerics.ai 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 https://www.science.org/doi/10.1126/science.ado9336, and presented by our CEO on the main stage of TED2025 https://www.youtube.com/watch?v=EnbfoFUFm2s. Evo was used to create the first AI gene therapy tool CRISPR-Cas9, and the first AI whole genome https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1 from scratch. Evo 2 https://www.nature.com/articles/s41586-026-10176-5, 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.
  • Radical Numerics http://radicalnumerics.ai 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 https://www.science.org/doi/10.1126/science.ado9336, and presented by our CEO on the main stage of TED2025 https://www.youtube.com/watch?v=EnbfoFUFm2s.
  • Evo was used to create the first AI gene therapy tool CRISPR-Cas9, and the first AI whole genome https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1 from scratch.
  • Evo 2 https://www.nature.com/articles/s41586-026-10176-5, 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.

Visa & Work Authorization

  • 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.
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This listing is sourced directly from Radical Numerics's careers page and normalized into a canonical job model.