Scientist, Cell Biology

Axiom • South San Francisco, CA

3d

About The Position

Build human cell models and multiplexed high-content assays to reveal how drugs cause toxicity and predict their effects in people. The role combines advanced human cell systems with imaging, transcriptomics, mass spectrometry, and drug-metabolism measurements to generate biological datasets for translational drug discovery and AI models.

The scientist will develop physiologically relevant systems and assays for studying drug-induced toxicity across the liver, kidney, heart, immune system, and pharmacokinetics. They will collaborate with robotics engineers to create reproducible, high-throughput experimental workflows, and with computational scientists, drug discovery teams, pharmaceutical companies, and AI labs to interpret results and guide drug-development decisions. The role emphasizes rigorous experimental design, reproducibility, biological relevance, data quality, and investigation of the mechanisms underlying drug-induced injury.

Requirements

• Deep understanding of cell biology and human physiology.
• Ability to invent and refine biological models and assays beyond established protocols.
• Commitment to reproducibility, biological relevance, data quality, and attention to detail.
• Ability to design rigorous 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 eagerness 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.
• Experience developing physiologically relevant models, including co-cultures, organoids, spheroids, and engineered tissues.
• Strong foundation in cell biology, molecular biology, physiology, and pharmacology.
• Expertise in high-content imaging, Cell Painting, fluorescence microscopy, live-cell imaging, and image-based phenotypic screening.
• Experience developing, miniaturizing, and automating assays for high-throughput 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.
• Strong experimental design and quantitative analysis skills, including dose-response analysis, statistical methods, assay validation, and quality control.

Responsibilities

• Build human cell models that predict drug-induced toxicity in the liver, kidney, heart, and immune system, as well as 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 cellular changes with underlying molecular mechanisms.
• Work 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 large-scale, high-quality biological datasets for toxicity prediction and drug development.
• Partner with pharmaceutical companies and AI labs to interpret data, uncover biochemical mechanisms, and guide drug discovery decisions.

What This Job Offers

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