---
title: 'Computational Scientist, Mass Spectrometry at Axiom'
canonical: 'https://feeny.ai/job/computational-scientist-mass-spectrometry-axiom-south-san-francisco-x3cdpcgqbqtv'
type: 'job'
last_seen: '2026-11-01'
---

# Computational Scientist, Mass Spectrometry at Axiom

- **Company:** Axiom
- **Location:** South San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-07-10
- **Last confirmed live:** 2026-11-01
- **Apply:** https://jobs.ashbyhq.com/axiombio/a3191175-40d4-4f75-aec2-18313600413a

## Job description

Description: Build computational tools and scientific AI agents that turn complex mass spectrometry data into molecular identities, quantitative measurements, and insights into drug translation.. Work with experimental scientists to develop reliable analytical workflows and training environments that teach frontier models to reason about chemistry.

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:

- Build computational tools and AI agents for LC-MS/MS feature detection, peak alignment, molecular identification, quantification, and quality control.
- Develop agents that predict molecular fragmentation and identify structures by combining mass spectra, chromatography, UV, and NMR evidence.
- Solve complex mixture analysis problems, including overlapping peaks, background interference, matrix effects, and uncertain compound identities.
- Build reliable workflows for relative and absolute quantification, accounting for calibration, measurement uncertainty, and compound-dependent signal responses.
- Combine analytical chemistry expertise with agents to investigate drug metabolism, identify reactive metabolites, and interpret drug-induced metabolic changes.
- Work with experimental scientists to generate datasets and verified reference measurements that reveal where computational methods and agents succeed or fail.
- Turn these experiments into benchmarks and reinforcement learning environments that teach frontier models increasingly complex chemical reasoning and analysis.
- Integrate metabolomics with imaging, transcriptomics, and proteomics to uncover mechanisms of drug toxicity and guide further experiments.

What we look for:

- Deep experience in computational LC-MS/MS, particularly untargeted metabolomics and complex mixture analysis.
- Strong programming skills and a track record of building reliable scientific software.
- A strong foundation in analytical chemistry, ionization, fragmentation, chromatography, and the physics of mass spectrometry.
- Experience developing algorithms or extending tools such as MZmine or OpenMS to solve challenging analytical problems.
- Excitement about building and using AI agents to interpret experimental data, test chemical hypotheses, and automate scientific analysis.
- Rigorous judgment about data quality, measurement uncertainty, and the evidence needed to support a molecular identification or biological conclusion.
- Curiosity and ownership, with a desire to work closely with experimental scientists and see your methods improve real experiments.

Technical skills we value:

- Python, NumPy, SciPy, pandas, SQL, and reproducible scientific software development; C++ is a plus.
- LC-MS/MS processing, including feature detection, peak integration, alignment, deconvolution, isotope and adduct annotation, and quality control.
- Untargeted metabolomics, spectral library matching, fragmentation prediction, molecular identification, and quantitative analysis.
- Tools and formats such as MZmine, OpenMS, MS-DIAL, SIRIUS, GNPS, and mzML.
- Cheminformatics and molecular representations, including RDKit, SMILES, molecular fingerprints, and structure similarity.
- LLM APIs, agent tool development, scientific data retrieval, and automated analysis workflows.
- Agent evaluation, trace analysis, and benchmarks with experimentally verified answers and uncertainty-aware scoring.
- Statistical methods for calibration, batch correction, differential abundance, and integration with imaging, transcriptomics, and proteomics.

## About Axiom

## Company Overview
- **One-liner**: Axiom builds an AI platform that predicts human drug toxicity from lab data, helping pharmaceutical companies develop safer medicines with less animal testing.
- **Entity Type**: Private (Seed round, April 2025)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2024
- **Founders**: Brandon White, Alex Beatson

## Core Business
- **Primary industry**: Biotechnology Research / Artificial Intelligence for Drug Discovery
- **Target customers**: B2B – pharmaceutical and biotech companies, drug discovery teams
- **Mission or purpose statement**: "Understand drug toxicity before it reaches humans" – using “frontier intelligence” to end unexpected drug toxicity and reduce clinical trial failures.

## Products & Services
- **Toxicity Prediction Platform**: An agentic AI system that connects experimental data (cell painting, mass spectrometry, metabolism assays) to clinical outcomes. Provides mechanistic, exposure-aware risk assessments early in discovery. (SaaS/API platform)

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Key Metric**: Seed round (April 2025) with participation from at least three investors; total funding amount not disclosed.
- **Notable Investors/Partners**: Amplify Partners, Abstract VC, Dimension Capital, Zetta Venture Partners, CRV; advisors include Jeffrey Dean (Google DeepMind), Yvonne Will (former Pfizer), Nicholas Meanwell (medicinal chemistry leader).
- **Growth Signals**: Headcount grew 108% YoY (22 employees); LinkedIn followers grew 287% YoY; website traffic up 277% YoY; actively hiring for ML, data, and platform engineering roles.

## Competitive Advantages
- **Human-relevant data core**: Models trained on primary human hepatocytes and clinical outcomes rather than animal models, directly addressing the 90% clinical failure rate.
- **Mechanistic AI**: Provides interpretable, exposure-aware risk profiles instead of black-box hazard flags, enabling chemists to modify compounds.
- **World-class advisory board**: Deep expertise from pharma safety, medicinal chemistry, and ML (e.g., Nicholas Meanwell, Yvonne Will, Jeffrey Dean).

## Strategic Focus
- Replace traditional animal-based toxicity tests with AI-driven, human-relevant predictions.
- Expand from liver toxicity (DILI) to other organ systems and broader safety readouts.
- Scale customer integrations via enterprise SaaS and collaborative research partnerships.

## Why Work Here
- **Culture**: Fast-growing startup (founded 2024) with a tight-knit team of ~22, emphasizing both scientific rigor and engineering excellence.
- **Work environment**: Hybrid/remote-friendly (roles listed as “In-Office or Remote”); offices in San Francisco with remote team members in Canada and Israel.
- **Engineering culture**: Strong ML and platform engineering focus; stack includes Python, PyTorch, React, Typescript, and scientific libraries.
- **Open roles** (as of mid-2026): Platform Engineer, Data Engineer, ML Researcher, Product Engineer, Computational Scientist (Mass Spectrometry) – posted on [jobs.ashbyhq.com](https://jobs.ashbyhq.com/axiombio) and [Built In](https://builtin.com/company/axiom-bio/jobs).

## Sources
1. [axi.om/company](https://www.axi.om/company)
2. [linkedin.com/company/axiombio](https://www.linkedin.com/company/axiombio)
3. [axi.om/mission](https://www.axi.om/mission)
4. [builtin.com/company/axiom-bio/jobs](https://builtin.com/company/axiom-bio/jobs)

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