--- title: 'Computational Scientist (Mass Spectrometry) at Axiom' canonical: 'https://feeny.ai/job/computational-scientist-mass-spectrometry-axiom-san-francisco-x3cdpcgqbqtv' type: 'job' last_seen: '2026-09-06' --- # Computational Scientist (Mass Spectrometry) at Axiom - **Company:** Axiom - **Location:** San Francisco, CA - **Employment:** full-time - **Posted:** 2026-07-10 - **Last confirmed live:** 2026-09-06 - **Apply:** https://jobs.ashbyhq.com/axiombio/a3191175-40d4-4f75-aec2-18313600413a ## Job description Charter: 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. About Axiom: Axiom is building an ecosystem to compound technology which will replace animal testing and, over time, reshape how clinical trials are run. We partner with leading organizations to transform capital into proprietary data and machine learning models, then deploy those models across the world’s largest pharmaceutical companies to improve how medicines are discovered and developed. It starts with deeply understanding the needs of drug hunters inside large pharma, especially around drug toxicity and safety. 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 invest billions into 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 and phase 1 clinical trials. What you will do: 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. What we are looking for: 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. ## 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. 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