Asks for

The ad says“Build, improve, and scale computational pipelines for untargeted LC-MS/MS analysis using tools such as MZmine, OpenMS, MS-DIAL, GNPS, Skyline, or custom internal software.”
The ad says“Analyze large-scale biological mass spectrometry datasets, primarily LC-MS/MS, across metabolomics, lipidomics, proteomics, and reactive metabolite workflows.”
The ad says“You can reason about metabolism, pathway disruption, lipid biology, protein changes, and drug-induced cellular stress.”
The ad says“Analyze large-scale biological mass spectrometry datasets, primarily LC-MS/MS, across metabolomics, lipidomics, proteomics, and reactive metabolite workflows.”
The ad says“Turn raw mass spec data into model-ready representations that can be used by machine learning systems and mechanistic reasoning agents.”
The ad says“We are looking for someone who can combine mass spectrometry expertise, computational depth, and biological judgment.”
The ad says“You can reason about metabolism, pathway disruption, lipid biology, protein changes, and drug-induced cellular stress.”
The ad says“Analyze large-scale biological mass spectrometry datasets, primarily LC-MS/MS, across metabolomics, lipidomics, proteomics, and reactive metabolite workflows.”
The ad says“Build, improve, and scale computational pipelines for untargeted LC-MS/MS analysis using tools such as MZmine, OpenMS, MS-DIAL, GNPS, Skyline, or custom internal software.”
The ad says“Build, improve, and scale computational pipelines for untargeted LC-MS/MS analysis using tools such as MZmine, OpenMS, MS-DIAL, GNPS, Skyline, or custom internal software.”
The ad says“Build, improve, and scale computational pipelines for untargeted LC-MS/MS analysis using tools such as MZmine, OpenMS, MS-DIAL, GNPS, Skyline, or custom internal software.”
The ad says“You can reason about metabolism, pathway disruption, lipid biology, protein changes, and drug-induced cellular stress.”
The ad says“Analyze large-scale biological mass spectrometry datasets, primarily LC-MS/MS, across metabolomics, lipidomics, proteomics, and reactive metabolite workflows.”
The ad says“Build, improve, and scale computational pipelines for untargeted LC-MS/MS analysis using tools such as MZmine, OpenMS, MS-DIAL, GNPS, Skyline, or custom internal software.”

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 do:

You will own major parts of Axiom’s computational mass spectrometry stack.

Analyze large-scale biological mass spectrometry datasets, primarily LC-MS/MS, across metabolomics, lipidomics, proteomics, and reactive metabolite workflows.
Build, improve, and scale computational pipelines for untargeted LC-MS/MS analysis using tools such as MZmine, OpenMS, MS-DIAL, GNPS, Skyline, or custom internal software.
Develop workflows for peak detection, alignment, normalization, annotation, batch correction, QC, feature filtering, compound identification, and downstream biological interpretation.
Turn raw mass spec data into model-ready representations that can be used by machine learning systems and mechanistic reasoning agents.
Work with biology, chemistry, ML, engineering, and lab teams to design, debug, and improve high-throughput LC-MS/MS assays.
Extract actionable biological insights from mass spec data, including pathway-level changes, metabolic signatures, lipid remodeling, protein abundance changes, and evidence for specific toxicity mechanisms.
Help build datasets that connect chemical structure, dose, exposure, cellular phenotype, biochemical state, and human toxicity outcomes.
Develop quality control systems for high-throughput mass spectrometry datasets, including instrument performance, sample quality, replicate concordance, batch effects, missingness, drift, and annotation confidence.
Collaborate with ML researchers to build models that use mass spec features to improve toxicity prediction.
Investigate where mass spec helps explain model errors, reveals missing biology, or identifies mechanisms not visible from imaging, transcriptomics, or standard biochemical assays.
Design new strategies for expanding Axiom’s mass spec data generation based on model performance, biological coverage, and customer needs.
Help make mass spectrometry data interpretable and useful to drug hunters, toxicologists, and Axiom’s internal AI agents.

What we are looking for:

We are looking for someone who can combine mass spectrometry expertise, computational depth, and biological judgment.

You might be a great fit if:

You have built computational workflows for untargeted LC-MS/MS metabolomics.
You have used mass spectrometry data to answer real biological questions, not just run pipelines.
You understand the messy reality of mass spec data: missingness, batch effects, adducts, isotopes, retention time drift, annotation uncertainty, instrument artifacts, and biological confounders.
You are comfortable moving from raw files to biological interpretation.
You can reason about metabolism, pathway disruption, lipid biology, protein changes, and drug-induced cellular stress.
You are excited by the idea of using mass spec data as training data for AI systems.
You want to build scalable infrastructure, not just analyze one-off datasets.
You care deeply about data quality, reproducibility, and scientific rigor.
You can work closely with wet lab scientists to improve experimental design and debug assays.
You want ownership over a critical scientific modality at an early company.
You are motivated by the mission of replacing animal testing and preventing clinical toxicity failures.