Values

The ad says“Transcriptomics, proteomics, mass spectrometry, ADME, or other high-dimensional biological datasets”
The ad says“High-throughput screening, assay development, automation, and experimental QC”
The ad says“High-throughput screening, assay development, automation, and experimental QC”
The ad says“Biological interpretation of model outputs and error modes”
The ad says“CellProfiler, Cellpose, napari, OpenCV, scikit-image, or related image analysis tools”
The ad says“CellProfiler, Cellpose, napari, OpenCV, scikit-image, or related image analysis tools”
The ad says“Statistical analysis, curve fitting, dose-response modeling, dimensionality reduction, clustering, classification, regression, and model evaluation”
The ad says“Statistical analysis, curve fitting, dose-response modeling, dimensionality reduction, clustering, classification, regression, and model evaluation”
The ad says“Statistical analysis, curve fitting, dose-response modeling, dimensionality reduction, clustering, classification, regression, and model evaluation”
The ad says“Statistical analysis, curve fitting, dose-response modeling, dimensionality reduction, clustering, classification, regression, and model evaluation”
The ad says“Statistical analysis, curve fitting, dose-response modeling, dimensionality reduction, clustering, classification, regression, and model evaluation”
The ad says“High-throughput screening, assay development, automation, and experimental QC”
The ad says“High-content imaging analysis, microscopy, morphology profiling, and image-based phenotyping”
The ad says“High-throughput screening, assay development, automation, and experimental QC”
The ad says“High-content imaging analysis, microscopy, morphology profiling, and image-based phenotyping”
The ad says“Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks”
The ad says“Transcriptomics, proteomics, mass spectrometry, ADME, or other high-dimensional biological datasets”
The ad says“High-content imaging analysis, microscopy, morphology profiling, and image-based phenotyping”
The ad says“Statistical analysis, curve fitting, dose-response modeling, dimensionality reduction, clustering, classification, regression, and model evaluation”
The ad says“High-content imaging analysis, microscopy, morphology profiling, and image-based phenotyping”
The ad says“CellProfiler, Cellpose, napari, OpenCV, scikit-image, or related image analysis tools”
The ad says“Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks”
The ad says“CellProfiler, Cellpose, napari, OpenCV, scikit-image, or related image analysis tools”
The ad says“Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks”
The ad says“Transcriptomics, proteomics, mass spectrometry, ADME, or other high-dimensional biological datasets”
The ad says“Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks”
The ad says“Statistical analysis, curve fitting, dose-response modeling, dimensionality reduction, clustering, classification, regression, and model evaluation”
The ad says“Scientific storytelling: turning complex analyses into clear, credible, inspiring narratives”
The ad says“CellProfiler, Cellpose, napari, OpenCV, scikit-image, or related image analysis tools”
The ad says“Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks”
The ad says“Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks”
The ad says“Statistical analysis, curve fitting, dose-response modeling, dimensionality reduction, clustering, classification, regression, and model evaluation”
The ad says“Transcriptomics, proteomics, mass spectrometry, ADME, or other high-dimensional biological datasets”

About Axiom

Axiom is building the closed-loop scientific AI system required to replace animal testing and, over time, much of human safety testing. We start with pharma’s hardest drug development toxicology problems. Those problems define the proprietary human biological data we generate through Axiom’s Data Factory. We use that data to train scientific AI, partnering with leading AI labs to improve frontier models while building our own specialist agentic harness to deploy the improved frontier models back into pharma. Each deployment reveals the next capabilities to build, creating a compounding loop across data, models, and drug development. Today, liver toxicity is our proving ground. Axiom is already helping leading pharmaceutical companies understand toxicity, identify its mechanism, and design safer drugs. Over time, we will expand across the major organ systems and build the experimental and agentic system of record for translational drug development. Our goal is to dramatically reduce the risk of testing new molecules in humans, enabling high throughput evaluation of efficacy in humans.

What you will do

You will help build the computational and biological foundation for Axiom’s toxicity prediction platform.

Own the exploration and analysis of massive multimodal toxicity datasets spanning high-content imaging, transcriptomics, proteomics, ADME, mass spec, and functional cellular readouts.
Identify subtle biological signals that distinguish safe compounds from toxic compounds across human-relevant systems such as liver, heart, kidney, and immune biology.
Turn noisy, high-dimensional experimental data into clear biological insights, robust features, quality metrics, and model-ready datasets.
Analyze high-content imaging and transcriptomic data from primary human hepatocytes and multicellular hepatic systems, including phenotypes related to mitochondrial dysfunction, cholestasis, lipid accumulation, lysosomal stress, ER stress, cytotoxicity, and cellular morphology.
Conduct detailed model error analyses to understand what biology our models capture, where they fail, and what new data or assays are needed to improve them.
Collaborate with ML researchers to improve models that predict human toxicity as a function of dose, exposure, Cmax, in vitro potency, chemical structure, and biological response.
Develop computational approaches for extracting meaningful signal from imaging, transcriptomic, proteomic, and biochemical assays.
Design and improve quality control systems for large-scale, high-throughput biological datasets.
Work closely with wet lab scientists to shape new assays optimized not just for biological plausibility, but for predictive modeling.
Partner with leading pharma and biotech teams to interpret molecule toxicity profiles and help them understand the biology driving model predictions.
Help invent the future of computational toxicology: AI systems that do not just classify compounds, but explain mechanisms, reason over evidence, and guide better drug design.

What we are looking for

We are looking for someone who is unusually strong at both biology and computation.

You are a biologist who taught yourself to code because existing tools were not good enough for the questions you wanted to answer.
You are a computational scientist who loves being close to the raw experimental data, not just abstracted datasets.
You are exceptional at finding signal in messy biological data.
You have strong taste for what matters scientifically and can distinguish real biological insight from noise, artifacts, and overfit correlations.
You are excited by high-content imaging, transcriptomics, assay development, and the possibility of building the world’s largest experimental-to-clinical datasets.
You are obsessive about data quality, reproducibility, and scientific rigor.
You can bridge wet lab protocols and machine learning models, understanding how experimental design, assay biology, feature extraction, and modeling choices all interact.
You are excited to work across many biological systems and mechanisms, including hepatotoxicity, mitochondrial toxicity, cholestasis, reactive metabolites, transporter biology, immune-mediated toxicity, kidney toxicity, cardiac toxicity, and more.
You want to do science that matters in the real world, not just publish papers.
You have the ambition to help build a generational company from the ground up.

Technical skills we value

We do not expect every candidate to have all of these, but we are especially excited by experience with:

Python, Pandas, NumPy, SciPy, scikit-learn, Jupyter notebooks
Statistical analysis, curve fitting, dose-response modeling, dimensionality reduction, clustering, classification, regression, and model evaluation
High-content imaging analysis, microscopy, morphology profiling, and image-based phenotyping
CellProfiler, Cellpose, napari, OpenCV, scikit-image, or related image analysis tools
Transcriptomics, proteomics, mass spectrometry, ADME, or other high-dimensional biological datasets
High-throughput screening, assay development, automation, and experimental QC
Biological interpretation of model outputs and error modes
Scientific storytelling: turning complex analyses into clear, credible, inspiring narratives