Computational Scientist

About the role

Axiom is creating AI systems to replace animal testing and legacy toxicity experiments with human-relevant predictive models. As a founding team member, you will bridge the gap between complex chemistry, biological data, and machine learning to improve how new medicines are discovered.

Key facts

What you'll do

• Analyze model performance across diverse chemical series, targets, and clinical toxicity endpoints.
• Collaborate with machine learning researchers to refine predictive models for human toxicity.
• Process and curate large-scale chemical datasets, including structural data, ADME properties, and clinical outcomes.
• Apply medicinal chemistry expertise to interpret model predictions and identify structure-toxicity relationships.
• Engage directly with drug discovery teams at top pharmaceutical and biotech companies to support program decisions.
• Design new experimental datasets based on identified model gaps and customer requirements.
• Use mechanistic agents to link chemical structures with biological readouts and clinical outcomes.
• Translate user feedback into improved workflows and visualizations for toxicologists and medicinal chemists.

Requirements

• Advanced degree in chemistry, computational chemistry, cheminformatics, medicinal chemistry, chemical biology, or equivalent industry experience.
• Deep understanding of the drug discovery lifecycle from hit identification to candidate selection.
• Proficiency in reasoning about potency, selectivity, PK, exposure, and clinical translatability.
• Ability to analyze large chemical datasets by combining data science with biological and chemical intuition.
• Experience communicating technical insights to senior drug discovery professionals.
• Strong perspective on the limitations of current preclinical safety models and how to advance them.

Skills & tools

• Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks.
• RDKit, Datamol, DeepChem, or similar cheminformatics libraries.
• Chemical structure processing including standardization, salt stripping, and scaffold analysis.
• Expertise in QSAR, ADME modeling, and toxicity prediction.
• Experience with dose-response modeling, uncertainty analysis, and model benchmarking.
• SQL and cloud-based data processing workflows.
• Knowledge of drug discovery datasets covering targets, assays, and clinical outcomes.

Practical notes

This role is based on-site at our San Francisco global headquarters. You will be working at the intersection of chemistry, biology, and software to influence active drug development programs currently utilized by 7 of the top 20 pharmaceutical companies.