Radical Numerics

Radical Numerics

Member of Technical Staff, Post-Training

San Francisco · Staff+

Work authorization required$26k-$42kDetected 126 days ago
PythonDistributed SystemsMachine LearningPyTorchA/B TestingBioinformaticsCRISPRResearchCommunication

About the role

  • You should be excited to run careful experiments, question whether the metrics reflect reality, and translate empirical findings into better recipes, datasets, and productively used models.
  • This role sits at that interface between fundamental research and practical engineering.

Responsibilities

  • Develop and tune post-training recipes.
  • Design and iterate on post-training stages, datasets, reward signals, and hyperparameters for biological world models.
  • Study how choices in data mixtures, objective design, curriculum, and training schedules affect model behavior.
  • Build evaluations that actually matter.
  • Collaborate with the science team to develop and refine evaluation suites for biological reasoning, scientific usefulness, long-context behavior, robustness, and model reliability, and to identify when existing benchmarks stop being informative and should be replaced with better ones.
  • Debug model behavior end-to-end. Investigate failure modes in training runs and model outputs, distinguish between signal and noise, and trace problems back to data, optimization, evaluation design, or systems issues.
  • Collaborate across research and engineering. Work closely with colleagues in training systems, architecture, and biology-facing research to ensure post-training methods are grounded in the realities of large-scale experimentation and downstream scientific use.
  • Ability to design careful experiments, interpret ambiguous results, and separate real effects from artifacts, bugs, or benchmark overfitting.

Requirements

  • Strong track record in ML research or engineering, especially in frontier-model training, post-training, alignment, evaluation, data quality, or related areas.
  • Proficiency in building production-quality software and research infrastructure, ideally in Python and PyTorch, with comfort debugging large-scale training workflows.
  • Excellent written and verbal communication skills, especially the ability to explain technical findings clearly across research, engineering, and scientific collaborators.

Nice to have

  • Experience with RLHF, RLAIF, preference optimization, reward modeling, rejection sampling, or other post-training methods for large models.
  • Experience designing or operating evaluation frameworks for model quality, reliability, safety, or scientific task performance.
  • Familiarity with synthetic data generation, annotation workflows, or expert-in-the-loop data collection.
  • Background in applied math, systems, computational biology, or another quantitative scientific field.
  • Contributions to open-source ML systems, model tooling, or research infrastructure.
  • Work in an environment that combines distributed systems, model architecture, and numerics research with real biological applications.
  • Join a collaborative culture that values rigor, creativity, and cross-disciplinary partnership across AI labs, biotechs, hospital systems, and 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

  • Competitive compensation, comprehensive benefits, and support for continual learning.

Benefits

  • Work on preference- and feedback-driven learning.

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.