About Axiom

Axiom is building the closed-loop scientific AI system required to replace animal testing and, over time, much of human safety testing. We start with pharma’s hardest drug development toxicology problems. Those problems define the proprietary human biological data we generate through Axiom’s Data Factory. We use that data to train scientific AI, partnering with leading AI labs to improve frontier models while building our own specialist agentic harness to deploy the improved frontier models back into pharma. Each deployment reveals the next capabilities to build, creating a compounding loop across data, models, and drug development. Today, liver toxicity is our proving ground. Axiom is already helping leading pharmaceutical companies understand toxicity, identify its mechanism, and design safer drugs. Over time, we will expand across the major organ systems and build the experimental and agentic system of record for translational drug development. Our goal is to dramatically reduce the risk of testing new molecules in humans, enabling high throughput evaluation of efficacy in humans.redict human drug outcomes dramatically better than animals and phase 1 clinical trials.

What you will do

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, stereochemistry, 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.

What we are looking for

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 organization.

• You might identify as a computational chemist, cheminformatics scientist, ML for chemistry researcher, medicinal chemist with strong computational skills, or drug discovery scientist who became deeply technical.

• You understand how real drug programs move from hit discovery to lead optimization to candidate selection.

• You can reason about potency, selectivity, physicochemical properties, ADME, PK, exposure, safety margins, and clinical translatability.

• You are excited by the challenge of connecting chemical structure to human outcomes.

• You understand the limitations of current preclinical safety models and have strong opinions about how they should be improved.

• You are comfortable analyzing large chemical datasets and drawing conclusions from a combination of data science, chemistry, and biological reasoning.

• You can work directly with pharma customers, earn the trust of senior drug hunters, and communicate technical insights clearly.

• You want to build tools that are not just scientifically interesting, but actually used to make decisions in real drug discovery programs.

Technical skills we value

We do not expect every candidate to have all of these, but we are especially excited by experience with:

• Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks

• RDKit, Datamol, DeepChem, or related cheminformatics tooling

• Chemical structure processing, standardization, salt stripping, stereochemistry handling, scaffold analysis, similarity search, clustering, and molecular fingerprints

• Large-scale chemical dataset curation and quality control

• QSAR, molecular property prediction, ADME modeling, exposure modeling, or toxicity prediction

• Dose-response modeling, curve fitting, calibration, benchmarking, uncertainty analysis, and model error analysis

• SQL, cloud data workflows, and large-scale data processing

• Drug discovery datasets involving targets, assays, potency, selectivity, ADME, PK, toxicology, or clinical outcomes

• Scientific presentation and storytelling for medicinal chemists, toxicologists, and drug discovery leadership