The posting
Description:
Build and run Axiom’s LC-MS/MS experiments to identify drug metabolites, measure changes in cellular metabolism and proteins, and uncover mechanisms of toxicity. Combine analytical expertise with robotics and scientific AI to generate reliable data and teach frontier models to predict human drug outcomes with insane accuracy.
About Axiom:
Axiom’s mission is to eliminate unexpected drug toxicity and unlock an abundance of ultra safe medicines. We are starting with the liver, where toxicity remains a leading cause of drug failure. Our high-throughput human liver safety platform combines advanced human cell systems with imaging, transcriptomics, mass spectrometry, and drug metabolism measurements. Our AI agents integrate these data with chemical structure, human exposure, and thousands of clinical reference compounds to predict whether a drug will be toxic in humans, why, and how to make it safer.
We work with seven of the world’s top twenty pharmaceutical companies to design safety into molecules from the earliest stages. This applied work feeds directly into our partnerships with leading frontier AI labs, turning real discovery challenges and experimental feedback loops into scientific skill trees and reinforcement learning environments that train increasingly superhuman scientists. We then deploy these frontier models to make cancer therapies safer, unlock treatments for chronic diseases that demand exceptional safety, revive promising drugs abandoned because of toxicity, and much more.
Our ultimate ambition is to build a superhuman AI agent that predicts how a drug will behave across all major human organ systems before the first patient receives it. With insanely accurate safety predictions, we aim to enable rapid, high-throughput testing of novel drugs in humans with zero risk of toxicity. This is the key to unlocking an abundance of ultra safe, effective medicines.
What you will do:
- Own Axiom’s LC-MS/MS operations across targeted and untargeted workflows, including chromatography, UV detection, mass spectrometry, and supporting equipment.
- Develop and run reactive-metabolism assays to identify drug metabolites and trapped reactive intermediates, using isotope-labeled controls to verify signals and support structural interpretation.
- Design metabolomics experiments that reveal how drugs alter cellular metabolism, connecting measurements with imaging, transcriptomics, and other indicators of toxicity.
- Establish proteomics workflows with Axiom’s biology and computational teams, from sample preparation and digestion through peptide separation and LC-MS/MS acquisition.
- Generate high-quality reference datasets from pure compounds and controlled mixtures, varying concentrations, matrices, chromatography, and fragmentation conditions to test molecular identification and quantification.
- Develop and qualify methods with rigorous controls, calibration, and acceptance criteria for sensitivity, selectivity, reproducibility, carryover, and matrix effects.
- Partner with computational scientists to turn experiments into benchmarks and training environments with experimentally verified answers that teach AI agents to interpret spectra, resolve mixtures, and investigate toxicity.
- Work with engineers to integrate agents into real-time QC, adaptive acquisition queues, method optimization, and lab-in-the-loop experiments.
- Maximize instrument uptime through preventive maintenance, troubleshooting, user-serviceable repairs, consumables management, and coordination with service engineers.
What we look for:
- Exceptional hands-on LC-MS/MS expertise, with a deep understanding of chromatography, ionization, fragmentation, and targeted and untargeted analysis.
- Experience developing and running reactive-metabolism, metabolomics, or proteomics experiments, with the ability to master unfamiliar workflows.
- Strong experimental judgment. You design controls, calibration strategies, and validation studies that distinguish real chemical and biological signals from contamination, interference, and instrument artifacts.
- The ability to interpret complex spectra and mixtures, assess uncertainty, and recognize when molecular identification requires additional evidence.
- Strong troubleshooting and maintenance skills, with the resourcefulness to keep instruments and supporting systems operating reliably.
- Excitement about developing experiments that teach AI agents to reason about chemistry, and working with engineers to automate QC, adapt methods, and close the loop between analysis and experimentation.
- End-to-end ownership. You identify what matters, move quickly from observation to action, and follow problems through to a verified resolution.
- Exacting standards for sample handling, reproducibility, documentation, and data quality, alongside the ability to train technicians and build dependable laboratory practices.
- Clear communication, generosity with your expertise, and the curiosity to work across analytical chemistry, biology, computation, and engineering.
Technical skills we value:
- LC-MS/MS operation and method development, including UHPLC, UV detection, high-resolution MS, and fragmentation methods.
- Instrument troubleshooting, preventive maintenance, vacuum systems, nitrogen generators, and sample preparation equipment.
- Reactive-metabolite trapping, metabolic incubations, stable-isotope labeling, and metabolite identification.
- Targeted and untargeted metabolomics, including extraction, chromatographic separation, and analysis of complex biological matrices.
- Proteomics sample preparation, protein digestion, peptide separation, and LC-MS/MS acquisition.
- Assay development and qualification, calibration, internal standards, matrix effects, recovery, carryover, and measurement uncertainty.
- Spectral interpretation, accurate-mass analysis, isotope patterns, adduct identification, and integration of LC-MS/MS with UV and NMR evidence.
- Experimental data analysis using vendor software and tools such as MZmine, OpenMS, MS-DIAL, Skyline, or SIRIUS; Python or R is a plus.
- Laboratory automation, instrument interfaces, and AI-assisted workflows for acquisition planning, quality control, and experimental analysis.
