Computational Scientist (Mass Spectrometry)

Axiom•San Francisco, CA

About The Position

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. 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.

Requirements

Combine mass spectrometry expertise, computational depth, and biological judgment.
Built computational workflows for untargeted LC-MS/MS metabolomics.
Used mass spectrometry data to answer real biological questions, not just run pipelines.
Understand 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.
Can 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.
Want to build scalable infrastructure, not just analyze one-off datasets.
Care deeply about data quality, reproducibility, and scientific rigor.
Can work closely with wet lab scientists to improve experimental design and debug assays.
Want ownership over a critical scientific modality at an early company.
Motivated by the mission of replacing animal testing and preventing clinical toxicity failures.
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

Nice To Haves

Intense, curious, technical, and deeply motivated by the mission.
Want ownership, ambiguity, and responsibility.
Excited to build the systems that turn complex biochemical measurements into AI models that drug hunters can actually use.
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.
Could work in big tech, academia, pharma, or biotech, but would not be satisfied there because they want to solve a harder and more consequential problem.

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 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.