Scientist, Cell Biology

Axiom • South San Francisco, CA

7h

About The Position

Axiom is seeking an experimental scientist to build human cell models and multiplexed, high-content assays that reveal how drugs cause toxicity and help predict their effects in people. The role focuses on developing physiologically relevant models and generating rigorous, large-scale biological datasets for translational drug discovery and AI-based toxicity prediction.

The scientist will develop models and assays spanning liver, kidney, heart, immune-system toxicity, and pharmacokinetic properties such as exposure and metabolism. The work combines advanced human cell systems with imaging and molecular measurements, and involves close collaboration with robotics engineers, computational scientists, drug-discovery teams, pharmaceutical companies, and AI labs. The position also contributes to reproducible, high-throughput experimental workflows and rigorous validation and quality-control standards.

Requirements

• Deep understanding of cell biology and human physiology.
• Ability to invent and refine biological models and assays to address questions that established protocols cannot answer.
• Strong attention to reproducibility, biological relevance, and data quality, including identifying subtle sources of error.
• Ability to design decisive experiments with strong controls and draw evidence-supported conclusions.
• Ability to interpret complex biological phenotypes and distinguish meaningful effects from noise, confounding factors, and experimental artifacts.
• Curiosity about cell biology, experimental technologies, and drug discovery, and willingness to master new methods.
• Ability to communicate complex science clearly and make a compelling case for scientific ideas.
• Experience with primary human cell culture, iPSC culture and differentiation, and cell engineering.
• Strong foundation in cell biology, molecular biology, physiology, and pharmacology.
• Experience developing, miniaturizing, and automating assays for high-throughput screening.
• Strong experimental-design and quantitative-analysis skills, including dose-response analysis, statistical methods, assay validation, and quality control.

Nice To Haves

• Experience developing physiologically relevant models, including co-cultures, organoids, spheroids, and engineered tissues.
• Expertise in high-content imaging, Cell Painting, fluorescence microscopy, live-cell imaging, and image-based phenotypic screening.
• Experience measuring cell viability, mitochondrial function, oxidative stress, ER stress, apoptosis, inflammation, and other functional responses.
• Familiarity with RNA sequencing, proteomics, metabolomics, and methods that combine multiple biological measurements.

Responsibilities

• Build human cell models that predict drug-induced toxicity in the liver, kidney, heart, immune system, and pharmacokinetic properties such as exposure and metabolism.
• Design and optimize physiologically relevant systems using primary human cells, multicellular cultures, organoids, and other advanced models.
• Develop high-content assays that reveal how drugs disrupt cellular function and cause injury.
• Combine live-cell imaging, transcriptomics, proteomics, and mass spectrometry to connect changes in cell behavior with molecular mechanisms.
• Collaborate with robotics engineers to build a generalizable robotics platform for assay development and reproducible, high-throughput experimental workflows.
• Establish validation and quality-control standards to ensure consistent results across cell batches, plates, operators, and laboratories.
• Investigate mechanisms of drug-induced injury and design experiments that distinguish meaningful biological effects from measurement artifacts.
• Partner with computational scientists and drug-discovery teams to generate high-quality, large-scale biological datasets for toxicity prediction and drug-development decisions.
• Work with pharmaceutical companies and AI labs to interpret data, identify biochemical mechanisms behind observed effects, and guide drug-discovery decisions.

What This Job Offers

• Job Type: Full-time
• Education Level: No Education Listed