Software Engineer, Platform Infrastructure

Description:

Build the secure, reliable platform that powers Axiom’s scientific AI agents, experimental data pipelines, and products used by the largest pharmaceutical companies and AI labs in the world.

About Axiom:

Axiom’s mission is to eliminate unexpected drug toxicity and unlock an abundance of ultra safe medicines. We are starting with the liver, where toxicity remains a leading cause of drug failure. Our high-throughput human liver safety platform combines advanced human cell systems with imaging, transcriptomics, mass spectrometry, and drug metabolism measurements. Our AI agents integrate these data with chemical structure, human exposure, and thousands of clinical reference compounds to predict whether a drug will be toxic in humans, why, and how to make it safer.

We work with seven of the world’s top twenty pharmaceutical companies to design safety into molecules from the earliest stages. This applied work feeds directly into our partnerships with leading frontier AI labs, turning real discovery challenges and experimental feedback loops into scientific skill trees and reinforcement learning environments that train increasingly superhuman scientists. We then deploy these frontier models to make cancer therapies safer, unlock treatments for chronic diseases that demand exceptional safety, revive promising drugs abandoned because of toxicity, and much more.

Our ultimate ambition is to build a superhuman AI agent that predicts how a drug will behave across all major human organ systems before the first patient receives it. With insanely accurate safety predictions, we aim to enable rapid, high-throughput testing of novel drugs in humans with zero risk of toxicity. This is the key to unlocking an abundance of ultra safe, effective medicines.

What you will do:

• Build and operate the shared infrastructure behind Axiom’s scientific data pipelines, AI agents, research workflows, and Translate product.

• Own cloud infrastructure, compute, storage, databases, and networking, making systems reliable, secure, scalable, and cost-efficient.

• Develop workflow orchestration that lets scientists, engineers, and agents run complex computational pipelines with clear dependencies, durable state, and recovery from failures.

• Build secure execution environments for agents, with controlled access to scientific tools, models, and data, strict customer isolation, and resource and budget limits.

• Make scientific results reproducible through data versioning, lineage, snapshots, and automated checks that catch stale inputs, inconsistent features, and incomplete outputs.

• Build fast development environments, CI, and deployment systems that let people and agents ship changes confidently across development, staging, and production.