Computational Scientist (Biology)

Axiom•San Francisco, CA

About The Position

Axiom is building a compounding ecosystem to replace animal testing and, over time, reshape how clinical trials are run. It starts with deeply understanding the needs of drug hunters inside large pharma. 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 build 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.

Requirements

Unusually strong at both biology and computation.
A biologist who taught yourself to code because existing tools were not good enough for the questions you wanted to answer.
A computational scientist who loves being close to the raw experimental data, not just abstracted datasets.
Exceptional at finding signal in messy biological data.
Strong taste for what matters scientifically and can distinguish real biological insight from noise, artifacts, and overfit correlations.
Excited by high-content imaging, transcriptomics, assay development, and the possibility of building the world’s largest experimental-to-clinical datasets.
Obsessive about data quality, reproducibility, and scientific rigor.
Can bridge wet lab protocols and machine learning models, understanding how experimental design, assay biology, feature extraction, and modeling choices all interact.
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.
Want to do science that matters in the real world, not just publish papers.
Ambition to help build a generational company from the ground up.
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

Responsibilities

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.