Computational Scientist; Medicinal Chemistry

Charter

Be a founding member of the team building the first accurate AI systems for replacing animal and legacy toxicity experiments with human-relevant predictive models.

You will help Axiom answer one of the most important questions in drug discovery:

• Given a molecule’s structure, potency, exposure, and biological response, will it be safe enough in humans?

And, eventually:

• How should we change the molecule to make it safer while preserving efficacy?

Axiom is building a compounding ecosystem to replace animal testing and, over time, reshape how clinical trials are run. It starts with deeply understanding the needs of drug hunters inside large pharma. Those needs shape the world-class datasets we build from scratch. We then use that data to advance our own ML research, while also collaborating with leading AI labs to improve frontier models’ ability to reason over Axiom’s data inside Axiom’s agent harness.

This creates a compounding loop: deeper customer understanding shapes the data we generate; better data improves frontier models, Axiom’s fine‑tuned models, and our agentic infrastructure; stronger models and tooling expand the capabilities we can offer; and those capabilities are forward deployed into pharma's drug discovery workflows, where scientists use them to solve the highest value drug discovery problems. In turn, this helps us identify the next problems to tackle.

Today, we are focused on solving drug‑induced liver injury through an integrated data and agentic system already being used by 7 of the top 20 pharma companies and several of the world’s most innovative biotechs. Over time, Axiom will build the world’s largest human datasets across all the major organ systems, paired with an agentic harness that uses this data to predict human drug outcomes dramatically better than animals.

You will sit at the center of Axiom’s chemistry, biology, modeling, and customer work.

• Lead the analysis of model outputs across chemical series, targets, modalities, mechanisms, and clinical toxicity endpoints.
• Identify where Axiom’s models perform well, where they fail, and what those failures reveal about chemistry, biology, exposure, or missing data.
• Work with ML researchers to improve models that predict human toxicity as a function of chemical structure, in vitro potency, biological response, dose, Cmax, ADME, and clinical context.
• Analyze large‑scale chemistry datasets across thousands to hundreds of thousands of compounds for model training, evaluation, benchmarking, and dataset design.
• Clean, curate, and structure chemical data, including compound identifiers, structures, salts, stereo chemistry, dose/exposure information, ADME properties, targets, annotations, and clinical outcomes.
• Use medicinal chemistry intuition to interpret model predictions, understand structure–toxicity relationships, and identify chemically meaningful patterns.
• Partner directly with top drug hunters at leading pharma and biotech companies to interpret model outputs and help them make better program decisions.
• Help design new experimental and molecular datasets based on model failures, customer needs, chemical space gaps, and real‑world drug discovery use cases.
• Work with Axiom’s mechanistic agent to connect chemical structure, biological readouts, phenotypic similarity, clinical outcomes, and proposed mechanisms of toxicity.
• Influence active drug programs by helping teams understand whether toxicity risk is driven by exposure, potency, off‑target biology, reactive metabolites, transporters, mitochondrial liability, cholestasis, immune mechanisms, or other drivers.
• Shape Axiom’s product by translating customer feedback into better model outputs, visualizations, analyses, and workflows for medicinal chemists and toxicologists.
• Help define how the best drug hunters in the world will use AI to design safer medicines.

We are looking for someone who can combine medicinal chemistry judgment with computational depth.

• You have an advanced degree in chemistry, computational chemistry, cheminformatics, medicinal chemistry, chemical biology, or equivalent experience inside a drug discovery…