About Axiom:
Axiom is building a compounding ecosystem to replace animal testing and, over time, reshape how clinical trials are run. It starts with deeply understanding the needs of drug hunters inside large pharma. Those needs shape the world-class datasets we build from scratch. We then use that data to advance our own ML research, while also collaborating with leading AI labs to improve frontier models’ ability to reason over Axiom’s data inside Axiom’s agent harness. This creates a compounding loop: deeper customer understanding shapes the data we generate; better data improves frontier models, Axiom’s fine-tuned models, and our agentic infrastructure; stronger models and tooling expand the capabilities we can offer; and those capabilities are forward deployed into pharma's drug discovery workflows, where scientists use them to solve the highest value drug discovery problems. In turn, this helps us identify the next problems to tackle. Today, we are focused on solving drug-induced liver injury through an integrated data and agentic system already being used by 7 of the top 20 pharma companies and several of the world’s most innovative biotechs. Over time, Axiom will build the world’s largest human datasets across all the major organ systems, paired with an agentic harness that uses this data to predict human drug outcomes dramatically better than animals.
What you will be doing:
Lead Axiom’s evolution into a world-class engineering company focused on enterprise ML software
Design and build the core infrastructure that powers Axiom’s enterprise ML systems, including model evaluation/deployment, model inference/serving, and customer data management
Architect scalable systems for inference, storage, and retrieval of chemical, biological, and clinical data
Deploy large-scale reasoning agents from research environments into production, integrating them into on-prem customer-facing products and infrastructure
Teach and empower scientists across ML, chemistry, and biology to become great engineers by instilling a great engineering culture
Various expertise which gets us interested:
Built SaaS products that store and process large volumes of customer data.
Worked directly with large enterprise customers and supported their complex software needs
Designed and developed large-scale machine learning systems covering data access, training, evaluation, and deployment
Handled the “messy” parts of ML deployment, such as evaluation pipelines, versioning, and monitoring
Built LLM-powered data systems, with a focus on research workflows and information retrieval
Key criteria:
Strong generalist software engineer with experience across cloud infrastructure,machine learning, backend systems, distributed systems
Enjoys working with enterprise customers and simplifying complex technical solutions to meet their needs
Built and deployed production systems used by large enterprise businesses
Invested in team growth particularly when it comes to building strong engineering culture across the company
Passionate about collaborating with researchers and scientists, helping them become strong engineers
Takes full ownership of the customer experience—deeply focused on reliability and all the ways things can go wrong
Demonstrates relentless
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What We Do
Axiom helps scientists eliminate molecular toxicity by providing the most accurate and affordable predictive models. Our proprietary dataset includes more than 100,000 molecules tested in pooled primary human liver cells, tens of thousands of molecules with pharmacokinetic measurements, and thousands of molecules with curated clinical outcome data. Axiom's AI models offer higher accuracy than advanced in vitro systems like 3d spheroids, deep mechanistic understanding which untangles mitochondrial toxicity, ER stress, ROS formation, cytotoxicity, and more, and precise risk assessment for any molecule at relevant clinical dosage and clinical exposure levels. Our models remove the need for costly physical experiments, giving more accurate and cheaper toxicity assessments, and empowering scientists to make better-informed decisions and bring safer drugs to the clinic.
