ML Researcher

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.

What you will do

You will help define Axiom’s core ML research agenda and build the models that power our product.

• Define end-to-end ML and agent systems spanning wet-lab data generation, data cleaning, feature extraction, representation learning, model training, evaluation, inference, deployment, and customer-facing outputs.

• Build novel models that learn the relationship between chemistry, biological response, dose, exposure, and human toxicity.

• Train large multimodal models on paired chemical structures, high-content cellular images, transcriptomics, proteomics, mass spectrometry, ADME, and clinical outcome data.

• Develop foundation models and representation-learning systems for biological images, molecules, and multimodal experimental readouts.

• Architect models that predict human toxicity as a function of dose, Cmax, in vitro potency, chemical structure, and biological state.

• Develop new ways to aggregate, pool, align, and interpret embeddings across assays, doses, timepoints, modalities, compounds, and biological systems.

• Work on contrastive learning, self-supervised learning, semi-supervised learning, multimodal learning, graph neural networks, biological image models, generative models, and mechanistic reasoning systems.

• Build models that can generalize across chemical space, mechanisms, targets, assays, and customer programs.

• Conduct rigorous error analysis to understand when models fail, why they fail, and what data would make them better.

• Collaborate with computational biologists, chemists, mass spec scientists, data engineers, and wet-lab teams to design experiments that maximally improve model performance.