Computational Scientist (Medicinal Chemistry)

Axiom is building the translational intelligence layer for drug discovery: AI systems that help drug hunters predict human toxicity, understand mechanism, and design safer molecules before they reach the clinic.

Unexpected toxicity remains one of the largest causes of drug failure. Today, medicinal chemists and drug discovery teams still rely on a patchwork of animal studies, legacy in vitro assays, historical intuition, and imperfect safety margins to decide which molecules to advance. We believe this can be dramatically better.

At Axiom, we generate large-scale human-relevant biological data across primary human cells, multicellular tissue systems, high-content imaging, transcriptomics, proteomics, ADME, and functional assays. To date, we've built the largest experimental-to-clinical dataset in the world and we are just getting started. We then build agents that reason over this data to predict toxicity, explain mechanisms, and help scientists design safer molecules.

We are looking for a computational scientist with deep medicinal chemistry judgment: someone who can reason across structure, potency, ADME, exposure, toxicity, and mechanism; work directly with world-class drug hunters; and help build AI systems that improve real drug discovery decisions.

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?

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

You will:

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  Lead the analysis of model outputs across chemical series, targets, modalities, mechanisms, and clinical toxicity endpoints.

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  Identify where Axiom’s models perform well, where they fail, and what those failures reveal about chemistry, biology, exposure, or missing data.

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  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.

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  Analyze large-scale chemistry datasets across thousands to hundreds of thousands of compounds for model training, evaluation, benchmarking, and dataset design.

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  Clean, curate, and structure chemical data, including compound identifiers, structures, salts, stereochemistry, dose/exposure information, ADME properties, targets, annotations, and clinical outcomes.

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  Use medicinal chemistry intuition to interpret model predictions, understand structure–toxicity relationships, and identify chemically meaningful patterns.

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  Partner directly with top drug hunters at leading pharma and biotech companies to interpret model outputs and help them make better program decisions.

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  Help design new experimental and molecular datasets based on model failures, customer needs, chemical space gaps, and real-world drug discovery use cases.

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  Work with Axiom’s mechanistic agent to connect chemical structure, biological readouts, phenotypic similarity, clinical outcomes, and proposed mechanisms of toxicity.

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  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.

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  Shape Axiom’s product by translating customer feedback into better model outputs, visualizations, analyses, and workflows for medicinal chemists and toxicologists.

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  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 might be a great fit if:

• 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.

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

Axiom is not a normal company, and this is not a normal computational chemistry role.

We are looking for people who are intense, curious, technically excellent, and deeply motivated by the mission. You should want ownership, ambiguity, and responsibility. You should be excited to work on a problem where the answer is not in a textbook and where progress requires inventing new ways to connect chemistry, biology, and clinical reality.

The people who thrive here:

• Move with urgency.

• Have exceptional taste.

• See what needs doing and do it.

• Care obsessively about scientific truth.

• Can go deep technically while staying focused on customer impact.

• Are comfortable challenging assumptions from ML, biology, chemistry, and pharma.

• Want to work with world-class drug hunters on real decisions.

• Raise the bar for everyone around them.

• Are not satisfied with incremental improvements.

• Want to build a generational company.

• We are looking for people who could work in big tech, pharma, biotech, or academia, but who would not be satisfied there because they want to solve a harder and more consequential problem.