Radical Numerics

Radical Numerics

Member of Technical Staff, AI Bio

San Francisco · Staff+

Work authorization required$26k-$42kDetected 127 days ago
PythonDistributed SystemsMachine LearningDeep LearningPyTorchLLMsAccessibilityBioinformaticsCRISPRMolecular BiologyResearch

About the role

  • 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 designing post-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.

Responsibilities

  • Design and run experiments applying large models to problems in genomics, regulatory biology, protein biology, or cellular systems.
  • Develop evaluation pipelines and benchmarks for biological tasks such as variant interpretation, gene regulation modeling, protein function prediction, and multimodal cellular modeling - and drive these capabilities toward grounded downstream biological impact.
  • Design biologically meaningful data representations and modeling schemes across sequence, molecular, and multimodal data modalities.
  • Collaborate with model architecture teams to integrate biological capabilities into next-generation foundation models.
  • Prototype new approaches for biological prediction and design using foundation models.
  • 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 downstream applications.

Requirements

  • 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.
  • Strong Python and ML tooling experience (PyTorch or JAX, experiment management, distributed training).

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 scaling ML experiments across large GPU clusters.
  • Research publications in ML for biology, chemistry, or related areas.
  • We believe biology is the most impactful and consequential application of AI.
  • Work on frontier AI systems in a collaborative culture that values rigor, creativity, and cross-disciplinary partnership across AI labs, biotechs, hospital systems, and national research institutes.
  • 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.

Compensation

  • $26k-$42k

Benefits

  • Strong background and intuition in machine learning and deep learning across large-scale generative architectures, from autoregressive LLMs to diffusion models.
  • 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.

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.