Impress employers and recruiters.
Choose from hundreds of resume examples.

Target Resume Now

About the Role

Axiom is building a compounding ecosystem to replace animal testing and reshape clinical trials. We use data to advance ML research and collaborate with AI labs to improve frontier models' ability to reason over our data. This creates a compounding loop where deeper customer understanding shapes data generation, better data improves models and infrastructure, stronger models expand capabilities, and these capabilities are deployed into drug discovery workflows. We are currently focused on solving drug-induced liver injury with an integrated data and agentic system used by top pharma companies and biotechs. Axiom aims to build the world's largest human datasets across major organ systems, paired with an agentic harness to predict human drug outcomes better than animals.

Responsibilities

Own the harness: the scaffolding, tooling, and infrastructure that turn frontier models into agents that do long-horizon scientific analysis
Build the data backbone: pipelines, storage, and systems for runtime context, agent trajectories, eval results, and training data
Build sandboxed execution environments with instant spin-up/tear-down, reproducible and deterministic enough for evals and RL
Design and run the eval systems: offline suites, test cases on production traces, LLM-as-judge pipelines, regression gates
Work closely with domain experts to encode their taste into measurable rubrics, golden sets, and review workflows
Build agent tools and adopt new methods, protocols, and patterns from the state of the art
Make every agent run observable and replayable: trace model calls, tool calls, and state transitions, and build debugging tooling
Engineer the context: memory, compaction, retrieval, and recovery for coherent long-horizon agent runs
Own the loop: retries, budget caps, stop conditions, output verification, permissions, and guardrails
Support ML research with environments, reward instrumentation, and rollout infra for RL on agentic tasks

Requirements

Engineers who've built with LLM APIs and shipped agentic systems: tool use, loops, and debugging experience
Built bespoke evaluation, monitoring, and RL env observability tooling
Can tackle deep technical challenges and own/ship simple, clean, maintainable code
High ownership: owns outcomes end to end and starts moving without a spec
Allergic to complexity: reaches for the simplest system that works and keeps it that way
Strong software engineer first, with infrastructure, platform, data, or devtools depth and production systems they're proud of
Instinctively asks 'how would we know if this is working?' and builds measurement alongside the feature
Reads an agent failure trace like a stack trace
Cares about reliability because environments that break silently poison evals and training data
Has a knack for surfacing important questions about agent improvement
Sharp and confident
Keeps up with a technical and sophisticated crowd
Comfortable getting in over their head and figuring it out
Thrives in a discipline with no playbook, as it's being invented
Curious about how things work: engineering/tinkering mindset, good at scavenging the state of the art
Passion for learning what 'good' looks like from domain experts and turning it into systems

Skills

Python
Modal
DuckDB
FastAPI
Docker
Containerization
Terraform
LLM APIs
Agentic systems
Tool use
Loops
Evaluation tooling
Monitoring tooling
RL env observability tooling
SvelteKit
Svelte 5
React

About the Company

Axiom is building a compounding ecosystem to replace animal testing and, over time, reshape how clinical trials are run.
It starts with deeply understanding the needs of drug hunters inside large pharma.
Those needs shape the world-class datasets we build from scratch.
We then use that data to advance our own ML research, while also collaborating with leading AI labs to improve frontier models’ ability to reason over Axiom’s data inside Axiom’s agent harness.
This creates a compounding loop: deeper customer understanding shapes the data we generate; better data improves frontier models, Axiom’s fine-tuned models, and our agentic infrastructure; stronger models and tooling expand the capabilities we can offer; and those capabilities are forward deployed into pharma's drug discovery workflows, where scientists use them to solve the highest value drug discovery problems.
In turn, this helps us identify the next problems to tackle.
Today, we are focused on solving drug-induced liver injury through an integrated data and agentic system already being used by 7 of the top 20 pharma companies and several of the world’s most innovative biotechs.
Over time, Axiom will build the world’s largest human datasets across all the major organ systems, paired with an agentic harness that uses this data to predict human drug outcomes dramatically better than animals.