Computational Scientist (Mass Spectrometry)

About the role

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.

What you will do

• 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.
• 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

• Able to combine mass spectrometry expertise, computational depth, and biological judgment.
• Experience building computational workflows for untargeted LC-MS/MS metabolomics.
• Experience using mass spectrometry data to answer real biological questions, not just run pipelines.
• Understanding of the messy reality of mass spec data: missingness, batch effects, adducts, isotopes, retention time drift, annotation uncertainty, instrument artifacts, and biological confounders.
• Comfortable moving from raw files to biological interpretation.
• Reasoning about metabolism, pathway disruption, lipid biology, protein changes, and drug-induced cellular stress.
• Excitement about using mass spec data as training data for AI systems.
• Desire to build scalable infrastructure, not just analyze one-off datasets.
• Care about data quality, reproducibility, and scientific rigor.
• Ability to work closely with wet lab scientists to improve experimental design and debug assays.
• Motivation to own a critical scientific modality at an early company.
• Motivation driven by the mission of replacing animal testing and preventing clinical toxicity failures.

Technical skills we value

• Experience with Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks.
• Experience with MZmine, OpenMS, MS-DIAL, XCMS, GNPS, Skyline, ProteoWizard, MaxQuant, DIA-NN, Spectronaut, or related tools.
• Experience with LC-MS/MS data formats such as mzML, mzXML, RAW, mzTab, mzIdentML, mzQuantML, or vendor-specific formats.
• Experience with peak picking, chromatographic alignment, feature grouping, deconvolution, annotation, normalization, and batch correction.
• Experience with metabolite, lipid, and peptide identification workflows.
• Experience with spectral libraries, molecular networking, fragmentation interpretation, adduct/isotope handling, and confidence scoring.
• Experience with statistical modeling, dimensionality reduction, clustering, differential abundance analysis, and pathway enrichment.
• Experience with large-scale data processing, SQL, cloud computing, workflow orchestration, and reproducible analysis pipelines.