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