Data Engineer

ABOUT AXIOM Axiom is building the translational intelligence layer for drug discovery: AI 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 fragmented experimental data, animal studies, literature evidence, and expert judgment to decide which molecules are safe enough to advance. We believe this can be dramatically improved. Axiom generates and curates massive multimodal datasets spanning chemical structures, primary human cell imaging, transcriptomics, proteomics, mass spectrometry, ADME, clinical outcomes, human exposure, and literature-derived toxicity evidence. To date, we've built the largest experimental-to-clinical dataset and we are just getting started. These datasets power the models and agents that help drug hunters understand toxicity risk, mechanism, and safety margin. We are looking for a data engineer to own the systems that make this possible. You will build the pipelines, infrastructure, APIs, and tooling that turn raw chemical, biological, and clinical data into ML ready training data and customer ready insights. This is a foundational role. The quality, scale, and reliability of Axiom’s data systems will directly determine how fast our science moves, how good our models become, and how much customers trust our product. CHARTER Be a founding member of the team building the first accurate AI systems for drug toxicity prediction: systems that can help replace animal studies and legacy experiments with human-relevant predictive models. You will build the data foundation that makes Axiom’s science and product possible. WHAT YOU WILL DO You will own core data infrastructure across Axiom’s research, ML, lab, and product systems. You will: - Build and maintain Axiom’s core data platform for ingesting, processing, validating, storing, and serving chemical, biological, clinical, and customer datasets. - Build Axiom’s LabOS: the software layer that connects lab protocols, compound logistics, assay execution, plate/well metadata, instrument outputs, QC checks, data processing, model inference, and customer-facing results. - Turn raw experimental outputs into clean, versioned, ML-ready datasets across high-content imaging, transcriptomics, proteomics, mass spectrometry, ADME, dose-response, and clinical outcome data. - Design simple, reliable APIs and data interfaces that let scientists, ML researchers, and product engineers access the data they need without fighting infrastructure. - Build LLM powered systems for literature research, clinical data extraction, evidence curation, and dataset generation. - Develop distributed systems for running large-scale LLM jobs that clean, normalize, deduplicate, and structure biological and clinical data. - Scale inference pipelines for image models, graph neural networks, chemical models, and mechanistic agents. - Automate ETL from diverse sources, including lab instruments, CRO outputs, public databases, customer files, internal research tables, cloud storage, and literature-derived datasets. - Create rigorous data validation, testing, monitoring, lineage, and observability systems so Axiom can trust the datasets that drive model training, evaluation, customer delivery, and scientific decisions. - Work closely with scientists to understand messy real-world data needs and translate them into robust infrastructure. - Support customer-facing data delivery systems, including raw data transfer, processed feature exports, model predictions, compound metadata, and versioned result packages. - Build infrastructure that accelerates every team at Axiom. WHAT WE ARE LOOKING FOR We are looking for someone who combines engineering taste, scientific curiosity, and extreme ownership. You might be a great fit if: - You have built large-scale data platforms used by many internal teams or ex