Computational Scientist (Mass Spectrometry)

Axiom is building the translational intelligence layer for drug discovery: AI systems that help scientists predict human toxicity earlier and more accurately than animal studies or legacy in vitro assays.

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 connect chemical structure to human-relevant mechanisms of toxicity.

We are looking for a computational scientist with deep mass spectrometry expertise to build this foundation.

Key Responsibilities

• Analyze large-scale biological mass spectrometry datasets (LC-MS/MS) across metabolomics, lipidomics, proteomics, and reactive metabolite workflows
• Build, improve, and scale computational pipelines for untargeted LC-MS/MS analysis using MZmine, OpenMS, MS-DIAL, GNPS, Skyline, or custom software
• Develop workflows for peak detection, alignment, normalization, annotation, batch correction, QC, feature filtering, compound identification, and biological interpretation
• Turn raw mass spec data into model-ready representations for ML systems and mechanistic reasoning agents
• Extract actionable biological insights including pathway-level changes, metabolic signatures, lipid remodeling, and protein abundance changes
• Build datasets connecting chemical structure, dose, exposure, cellular phenotype, biochemical state, and human toxicity outcomes
• Develop quality control systems for high-throughput mass spectrometry datasets
• Collaborate with ML researchers to build models using mass spec features for toxicity prediction
• Design new strategies for expanding mass spec data generation based on model performance and biological coverage

Technical Skills Valued

• Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks
• MZmine, OpenMS, MS-DIAL, XCMS, GNPS, Skyline, ProteoWizard, MaxQuant, DIA-NN, Spectronaut
• LC-MS/MS data formats (mzML, mzXML, RAW, mzTab)
• Peak picking, chromatographic alignment, deconvolution, annotation, normalization, batch correction
• Metabolite, lipid, and peptide identification workflows
• Spectral libraries, molecular networking, fragmentation interpretation
• Statistical modeling, dimensionality reduction, clustering, differential abundance analysis, pathway enrichment
• Large-scale data processing, SQL, cloud computing, workflow orchestration

Ideal Candidate

• Built computational workflows for untargeted LC-MS/MS metabolomics
• Used mass spectrometry data to answer real biological questions
• Understands the messy reality of mass spec data: missingness, batch effects, adducts, isotopes, retention time drift, annotation uncertainty
• Can reason about metabolism, pathway disruption, lipid biology, protein changes, and drug-induced cellular stress
• Excited by using mass spec data as training data for AI systems