Axiom is building the translational intelligence layer for drug discovery: AI systems that help scientists predict human toxicity earlier, more accurately, and more mechanistically than animal studies or legacy in vitro assays.
Unexpected toxicity remains one of the largest reasons drug programs fail. Today, drug discovery teams still rely on animal studies, low-dimensional assays, and fragmented expert judgment to decide which molecules are safe enough to advance. We believe this can be dramatically improved.
To predict toxicity, we need to understand what molecules actually do inside human cells and tissues. Mass spectrometry is one of the most important tools for that mission. It lets us observe the biochemical state of cells, identify metabolic liabilities, detect lipid and metabolite changes, understand pathway disruption, and eventually connect chemical structure to human-relevant mechanisms of toxicity.
We are looking for a computational scientist with deep mass spectrometry expertise to help build this foundation. You will develop and scale computational workflows for LC-MS/MS data, extract biological signal from complex biochemical datasets, and help turn mass spec into a core modality for Axiom’s AI toxicity prediction platform.
CharterBe a founding member of the team building the first accurate AI systems for drug toxicity prediction: systems that can help replace animal studies and legacy lab experiments with human-relevant models.
What you will doYou will own major parts of Axiom’s computational mass spectrometry stack.
You will:
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.
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.
We do not expect every candidate to have all of these, but we are excited by experience with:
Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks
MZmine, OpenMS, MS-DIAL, XCMS, GNPS, Skyline, ProteoWizard, MaxQuant, DIA-NN, Spectronaut, or related tools
LC-MS/MS data formats such as mzML, mzXML, RAW, mzTab, mzIdentML, mzQuantML, or vendor-specific formats
Peak picking, chromatographic alignment, feature grouping, deconvolution, annotation, normalization, and batch correction
Metabolite, lipid, and peptide identification workflows
Spectral libraries, molecular networking, fragmentation interpretation, adduct/isotope handling, and confidence scoring
Statistical modeling, dimensionality reduction, clustering, differential abundance analysis, and pathway enrichment
Large-scale data processing, SQL, cloud computing, workflow orchestration, and reproducible analysis pipelines
Axiom is not a normal company, and this is not a normal mass spectrometry role.
We are looking for people who are intense, curious, technical, and deeply motivated by the mission. You should want ownership, ambiguity, and responsibility. You should be excited to build the systems that turn complex biochemical measurements into AI models that drug hunters can actually use.
The people who thrive here:
Move with urgency.
Have exceptional scientific taste.
See what needs doing and do it.
Care obsessively about data quality.
Can debug both code and experiments.
Are comfortable living between biology, chemistry, instrumentation, and machine learning.
Want to build infrastructure that scales to massive datasets.
Are not satisfied with standard pipelines when better approaches are needed.
Raise the bar for everyone around them.
Want to build a generational company.
We are looking for people who could work in big tech, academia, pharma, or biotech, but who would not be satisfied there because they want to solve a harder and more consequential problem.
