axiombio - Computational Scientist (Mass Spectrometry)
Requirements
• 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.
Responsibilities
• You will 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.
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