AI Agent Orchestration Engineer

Axiom; San Francisco (CA); Hybrid; USD 180,000 - 260,000; Full time

Job summary

Axiom is building a compounding ecosystem to replace animal testing and reshape how clinical trials are run. We focus on drug‑induced liver injury and leverage a data and agentic harness to power ML research.

You will own the harness, pipelines, and infrastructure that turn frontier models into reliable agents, and you’ll work with domain experts to codify evaluation rubrics. This is a hands‑on role for engineers who ship clean, scalable systems and stay ahead of state‑of‑the‑art methods in AI

Qualifications

• Owns and ships end‑to‑end systems for agentic workflows.
• Architects data pipelines, storage, and runtime context for agents.
• Build sandboxed, deterministic environments for evals and RL.
• Design and run offline eval suites and LLM‑as‑judge pipelines.
• Collaborates with domain experts to encode rubrics and review workflows.
• Keeps agent runs observable with traceable model/tool/state transitions.
• Develops memory, retrieval, and recovery to maintain coherence over long horizons.
• Owns retry loops, budget caps, stop conditions, and guardrails.
• Supports ML research with environments and rollout infra for RL tasks.

Responsibilities

• Own the harness, tooling, and infrastructure for agent systems.
• Build the data backbone: pipelines, storage, contexts, and agent trajectories.
• Create sandboxed environments with instant spin‑up/tear‑down and deterministic evals.
• Design/run eval systems: offline suites, LLM‑as‑judge, regression gates.
• Encode expert taste into rubrics, golden sets, and review workflows.
• Build tools the agent needs and stay updated with new methods and patterns.

Skills

Python

Modal

DuckDB

FastAPI

Docker

Containerization

Terraform

LLM APIs

Tools

SvelteKit

Svelte 5

React

Job description

Axiom is building a compounding ecosystem to replace animal testing and reshape how clinical trials are run. We focus on drug‑induced liver injury and leverage a data and agentic harness to power ML research.

You will own the harness, pipelines, and infrastructure that turn frontier models into reliable agents, and you’ll work with domain experts to codify evaluation rubrics. This is a hands‑on role for engineers who ship clean, scalable systems and stay ahead of state‑of‑the‑art methods in AI