Computational Biologist for Toxicology AI Innovation

Axiom Bio; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Axiom Bio in San Francisco is seeking a biology-focused ML/data scientist to advance our closed-loop toxicology AI platform. You will analyze multimodal toxicity datasets, extract actionable signals, and bridge wet-lab data with machine learning to model mechanisms of drug-induced toxicity.

You will collaborate with ML researchers, develop high-content imaging assays, and help build scalable data pipelines for predicting safety profiles in drug development.

Qualifications

• Solid mathematical and statistical background in data analysis, modeling, and statistics.
• Experience with high-content imaging and image analysis workflows.
• Proficiency with CellProfiler/Cellpose for imaging data analysis.
• Experience with high-throughput screening and assay development.
• Ability to bridge biology and machine learning through coding.
• Strong track record of publishing scientific work.

Responsibilities

• Drive exploration and analysis of multimodal toxicity datasets.
• Transform high-dimensional biological data into clear, actionable signals.
• Analyze ML models to understand learned features and failure modes.
• Develop methods at the intersection of computation and biology to understand biological mechanisms.
• Model relationships between chemical structures and biological responses.
• Collaborate with ML researchers to improve model accuracy.
• Develop high-content imaging assays for predictive tissue toxicity datasets.
• Create quality control processes for large imaging datasets.
• Support drug discovery teams in understanding toxicity profiles.

Skills

Mathematical statistics

Imaging data analysis

Cellprofiler

Cellpose

High content imaging

Cell painting

Assay development

High throughput screening

Automation

Strong coding ability

Biology & CS bridging

Tools

Cellprofiler

Cellpose

Job description

Axiom Bio in San Francisco is seeking a biology-focused ML/data scientist to advance our closed-loop toxicology AI platform. You will analyze multimodal toxicity datasets, extract actionable signals, and bridge wet-lab data with machine learning to model mechanisms of drug-induced toxicity.

You will collaborate with ML researchers, develop high-content imaging assays, and help build scalable data pipelines for predicting safety profiles in drug development.