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About the Role

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

Responsibilities

• 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.
• 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 for 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.
• 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.
• 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 other data types.
• 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.

Requirements

• 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.
• Understand the complexities of mass spec data, including missingness, batch effects, adducts, isotopes, retention time drift, annotation uncertainty, instrument artifacts, and biological confounders.
• Comfortable moving from raw files to biological interpretation.
• Ability to reason about metabolism, pathway disruption, lipid biology, protein changes, and drug-induced cellular stress.
• Excited by the idea of using mass spec data as training data for AI systems.
• Desire to build scalable infrastructure.
• Care deeply about data quality, reproducibility, and scientific rigor.
• Ability to work closely with wet lab scientists to improve experimental design and debug assays.
• Desire for ownership over a critical scientific modality at an early company.
• Motivated by the mission of replacing animal testing and preventing clinical toxicity failures.
• Intense, curious, technical, and deeply motivated by the mission.
• Desire for ownership, ambiguity, and responsibility.
• Excited to build systems that turn complex biochemical measurements into AI models.
• 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.
• Comfortable living between biology, chemistry, instrumentation, and machine learning.
• Want to build infrastructure that scales to massive datasets.
• Not satisfied with standard pipelines when better approaches are needed.
• Raise the bar for everyone around them.
• Want to build a generational company.
• Seeking a role with higher impact than typically found in big tech, academia, or pharma/biotech.

Skills

• 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, mzIdentML, mzQuantML, vendor-specific)
• Peak picking
• Chromatographic alignment
• Feature grouping
• Deconvolution
• Annotation
• Normalization
• Batch correction
• Metabolite identification workflows
• Lipid identification workflows
• Peptide identification workflows
• Spectral libraries
• Molecular networking
• Fragmentation interpretation
• Adduct/isotope handling
• Confidence scoring
• Statistical modeling
• Dimensionality reduction
• Clustering
• Differential abundance analysis
• Pathway enrichment
• Large-scale data processing
• SQL
• Cloud computing
• Workflow orchestration
• Reproducible analysis pipelines

Location

• Remote

Work Type

• Full-time

Experience Level

• Founding team member
• Senior

About the Company

• Axiom is building a compounding ecosystem to replace animal testing and, over time, reshape how clinical trials are run.
• We deeply understand the needs of drug hunters inside large pharma, shaping the world-class datasets we build from scratch.
• We use this data to advance our own ML research and collaborate with leading AI labs to improve frontier models’ ability to reason over Axiom’s data.
• This creates a compounding loop: deeper customer understanding shapes data generation; better data improves models and infrastructure; stronger models expand capabilities; and these capabilities are deployed into pharma's drug discovery workflows.
• Today, we focus on solving drug-induced liver injury through an integrated data and agentic system used by 7 of the top 20 pharma companies and several innovative biotechs.
• Over time, Axiom will build the world’s largest human datasets across all major organ systems, paired with an agentic harness to predict human drug outcomes better than animals.