Software Engineer, Platform Infrastructure

Digitailor; South San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Axiom is seeking a highly capable Infrastructure Engineer to build and operate the shared platform for scientific data pipelines, AI agents, and Translate product. You will own cloud infrastructure, compute, storage, databases, and networking to ensure reliability, security, and cost efficiency.

You will implement workflow orchestration for complex computational pipelines, enable secure agent execution environments, and improve observability across systems.

Qualifications

• Expertise in building reliable production systems and distributed infrastructure.
• Experience with AI agents and workflows is a plus.
• Strong security and data governance mindset.

Responsibilities

• Build and operate infrastructure for AI data pipelines and agents.
• Own cloud infrastructure, compute, storage, databases, and networking for reliability and cost efficiency.
• Develop workflow orchestration for complex computational pipelines with clear dependencies and recovery.
• Create secure execution environments with proper access controls and data isolation.
• Implement observability through logs, metrics, and tracing for quick failure diagnosis.
• Collaborate with scientists and engineers to reduce operational bottlenecks and enable self-service tools.

Skills

Python

SQL

Async programming

APIs

Backend systems

AWS

Docker

Terraform

Cloud networking

Infrastructure as Code

Data versioning

CI/CD

GitHub Actions

Observability

TypeScript

SvelteKit

Tools

Modal

DuckDB/MotherDuck

Postgres

S3

Parquet

Datadog

Job description

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. Own authentication, permissions, secrets, and workload identity across internal systems, customer workspaces, laboratory partners, and model providers. Make systems observable through logs, metrics, traces, execution histories, and alerts that help people and agents investigate failures and verify results. Manage database migrations, backups, and recovery, and resolve production issues across Axiom’s platform and infrastructure providers. Work directly with scientists, ML researchers, and product engineers to remove operational bottlenecks and turn recurring manual work into reliable, self-service tools. What we look for: Exceptional software engineering ability and experience building reliable production systems, with depth in infrastructure, distributed systems, platform engineering, or developer tools. Strong systems judgment. You simplify complex problems, choose clear abstractions, and build systems that remain understandable as they grow. End-to-end ownership. You identify what matters, make progress without a detailed specification, and follow through from design to deployment and operation. An instinct for reliability and correctness. You anticipate failure modes, make failures visible, and prevent silent errors from reaching scientific results or customers. Strong security judgment. You understand permissions, credentials, isolation, and trust boundaries, and make secure workflows easy to use. A talent for debugging across application code, databases, networks, cloud services, and execution environments. Experience building with AI agents and a drive to make them effective users of infrastructure, tools, and data, with appropriate controls and verification. Care for developer and scientist productivity. You notice recurring friction and replace manual work with dependable tools and automation. Clear communication, intellectual curiosity, and the willingness to learn biology, chemistry, and ML to build better systems for scientific discovery. Technical skills we value: Python, SQL, asynchronous programming, APIs, and production backend systems. AWS, Modal, Docker, Terraform, cloud networking, and infrastructure as code. DuckDB/MotherDuck, Postgres, S3, Parquet, database migrations, backups, and recovery. Workflow orchestration, dependency management, distributed execution, checkpointing, retries, and durable state. Data versioning, lineage, reproducible pipelines, and automated consistency checks. IAM, SSO, OAuth/OIDC, workload identity, secrets management, and customer isolation. Agent execution environments, sandboxing, tool access, model APIs, and resource and budget controls. CI/CD, GitHub Actions, isolated development environments, automated testing, and deployment strategies. Observability, including structured logs, metrics, tracing, alerting, Datadog, and production debugging. TypeScript, SvelteKit, Vercel, and internal developer tools are a plus.