Member of Technical Staff, Post-Training

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.

As a Member of Technical Staff, Post‑Training at Radical Numerics, you will develop the training and evaluation loops that shape biological world models after pretraining. You will work on the methods, data, and infrastructure required to improve model behavior on real scientific tasks: reasoning over long biological context, following complex objectives, making useful predictions, and interacting reliably with downstream tools and workflows.

This is a hands‑on role for someone who wants to both build systems and deepen understanding. 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.

We believe the next generation of biological foundation models will require not only new and improved pretraining recipes, but also innovation on post‑training: the work that turns a powerful base model into a system that is useful, steerable, robust, and scientifically productive. This role sits at that interface between fundamental research and practical engineering.

• 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.
• Work on preference‑and‑feedback‑driven learning. Explore methods such as preference modeling, reward modeling, synthetic feedback, or related post‑training approaches that improve how models respond to scientific tasks and constraints.
• Improve data for post‑training. Help define, curate, or generate high‑quality post‑training datasets, including expert‑informed data, synthetic data, and task‑specific examples grounded in biological workflows.
• Study scaling in post‑training. Measure how performance changes with dataset size, recipe complexity, compute budget, and model family. Use those results to guide what we scale next and what new directions are worth exploring.
• 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.

• Strong track record in ML…