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 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
Skills Required
- Strong generalist software engineer with experience across cloud infrastructure, machine learning, backend systems, distributed systems
- Design and build core infrastructure for 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 into production and integrate into on-prem customer-facing products and infrastructure
- Built and deployed production systems used by large enterprise businesses
- Enjoys working with enterprise customers and simplifying complex technical solutions to meet their needs
- Invested in team growth and building strong engineering culture across the company
- Passionate about collaborating with researchers and scientists and teaching engineering practices
- Takes full ownership of the customer experience with strong focus on reliability and failure modes
- 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 ML deployment challenges such as evaluation pipelines, versioning, and monitoring
- Built LLM-powered data systems focused on research workflows and information retrieval
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.
