Charter:
Join Axiom as a founding team member and help build a technology ecosystem that will replace animal testing and ultimately reshape clinical trials through agentic systems that can accurately predict human outcomes.
About Axiom:
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.
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.
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