Computational Scientist (Mass Spectrometry)

Axiom Bio; San Francisco (CA); On-site; USD 180,000 - 230,000; Full time

Job summary

Axiom Bio is seeking a Computational Scientist in San Francisco to build and scale LC-MS/MS analytical capabilities for toxicology-focused AI models. You will work with experimental scientists and AI researchers to develop new computational methods and computational tools to analyze large datasets across thousands of compounds.

Role focuses on untargeted metabolomics, algorithm development, and integration of multiple data types (imaging, transcriptomics) to understand drug toxicity, requiring

Qualifications

• Deep experience with computational LC-MS/MS, especially untargeted metabolomics.
• Strong programming skills and experience building scientific software.
• A strong understanding of analytical chemistry and the physics of mass spectrometry.
• Experience developing new algorithms or extending tools such as MZmine or OpenMS.
• Enjoy solving hard scientific problems and applying work to real experiments.

Responsibilities

• Build and improve computational tools for LC-MS/MS analysis, including feature detection, peak alignment, molecular identification, quantification, and QC.
• Develop methods to identify molecular structures from mass spectra and other measurements such as chromatography, UV, and NMR.
• Solve challenges in complex mixture analysis, drug metabolism, reactive metabolite identification, and cellular metabolomics.
• Build automated workflows to process large experimental datasets accurately and reliably.
• Turn mass spectrometry experiments into benchmarks and training environments for AI models.
• Combine mass spec with imaging, transcriptomics, and other measurements to understand drug toxicity.

Skills

LC-MS/MS experience

Scientific software development

Analytical chemistry understanding

Algorithm development

Mass spectrometry data analysis

Programming

Tools

MZmine

OpenMS

Job description

Axiom is building AI systems to predict human drug toxicity and ultimately replace animal testing. We generate large amounts of experimental data from human cell systems and use that data to understand how drugs behave in humans and train AI to do the same. Mass spectrometry is becoming a core part of our platform. We're using it to identify and quantify molecules in complex samples, understand how drugs are metabolized, identify toxic metabolites, and measure how drugs change cellular chemistry. We're looking for a computational scientist to help build this capability from the ground up. You'll work closely with our experimental scientists and AI researchers to solve difficult analytical problems, develop new computational methods, and scale them across thousands of compounds.

What You'll Do

• Build and improve computational tools for LC-MS/MS analysis, including feature detection, peak alignment, molecular identification, quantification, and QC.


• Develop methods to identify molecular structures from mass spectra and other experimental measurements, including chromatography, UV, and NMR.


• Solve challenging problems in complex mixture analysis, drug metabolism, reactive metabolite identification, and cellular metabolomics.


• Build automated workflows that can process large experimental datasets accurately and reliably.


• Help turn mass spectrometry experiments into benchmarks and training environments that teach AI models to perform scientific analysis.


• Combine mass spec with imaging, transcriptomics, and other measurements to better understand drug toxicity.

What We're Looking For

• Deep experience with computational LC-MS/MS, especially untargeted metabolomics.


• Strong programming skills and experience building scientific software.


• A strong understanding of analytical chemistry and the underlying physics of mass spectrometry.


• Experience developing new algorithms or extending existing tools such as MZmine or OpenMS.


• Someone who enjoys solving hard scientific problems and wants to see their work used in real experiments.

Nice to Have

• Computational proteomics or lipidomics.


• Drug metabolism and reactive metabolite analysis.


• Machine learning for mass spectrometry.


• NMR analysis or molecular structure elucidation.