ML Researcher

ABOUT AXIOM Axiom is building the translational intelligence layer for drug discovery: AI and agentic systems that help scientists predict human toxicity earlier, more accurately, and more mechanistically than animal studies or legacy in vitro assays. Unexpected toxicity is one of the largest reasons drug programs fail. Today, drug discovery teams still rely on animal studies, fragmented assays, and expert judgment to decide which molecules are safe enough to advance. We believe this can be dramatically improved. At Axiom, we generate and curate massive multimodal datasets spanning chemical structures, primary human cell imaging, multicellular tissue systems, transcriptomics, proteomics, mass spectrometry, ADME, dose-response curves, clinical outcomes, and human exposure. To date, we've built the largest experimental-to-clinical dataset in the world and we are just getting started. We use these datasets to train models and agents that connect chemistry, biology, mechanism, and clinical risk. We are looking for a machine learning researcher to help define and build the core AI systems behind Axiom: models that learn from human-relevant experimental biology, predict toxicity at clinically meaningful exposures, explain mechanisms, and eventually help scientists design safer molecules. This is an end-to-end ML research role. You will work across data generation, data processing, model architecture, training, agentic workflows, evaluation, deployment, and product. You will build systems that drug hunters use to improve their drug discovery outcomes. 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 answer one of the hardest questions in drug discovery: - Given a molecule’s structure, potency, exposure, and biological response, will it be toxic in humans — and why? WHAT YOU WILL DO You will help define Axiom’s core ML research agenda and build the models that power our product. You will: - 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. - Help build Axiom’s mechanistic agents: systems that reason over experimental data, compare compounds to mechanistic neighbors, explain toxicity mechanisms, and guide scientific decisions. - Own the research-to-product loop: prototype, train, evaluate, ship, observe real usage, improve, and repeat. - Ship insanely great models and products to customers. RESEARCH AREAS WE ARE EXCITED ABOUT We are