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Anthrogen is engineering post-modality biology by developing AI systems that design modular biological machines and the experimental infrastructure to validate them. The company is a small, high-density team in San Francisco focused on frontier AI and biological systems validation.
As a Research Associate in Biology, you will be the hands-on scientist responsible for executing and improving experimental workflows that validate computationally designed biological systems. Your core mission is to build and characterize designed proteins with bench work that the team can trust, bridging the gap between AI-proposed sequences and validated biological function.
Key responsibilities include:
- Execute molecular biology workflows including cloning, PCR, transformations, and construct verification with meticulous sample tracking and reproducibility
- Express and purify proteins, assess material quality, and prepare samples for binding and functional experiments
- Run established assays with appropriate controls and collaborate with Platform scientists to develop and troubleshoot new assays
- Maintain clear experimental records, perform initial data analysis, and proactively flag unexpected results or quality issues
- Continuously improve protocols and lab workflows to increase throughput without compromising data integrity
You will work closely with Platform scientists, learn new methods, and take ownership of experimental workflows from construct preparation through sample quality control and assay execution. The role emphasizes both protocol adherence and scientific judgment—knowing when a protocol is no longer answering the question is as important as following it correctly.
Requirements:
- Excellent hands-on molecular biology or protein biochemistry skills demonstrated through substantial bench work
- BS/MS in a relevant field (biology, biochemistry, molecular biology, or related discipline) or equivalent practical laboratory experience
- Ability to independently run known workflows, recognize common failure modes, and troubleshoot systematically
- Meticulous attention to samples, controls, calculations, and records; ability to explain how you know an experiment worked
- Quick learner who communicates clearly and seeks help proactively to prevent avoidable errors
- Genuine ownership of scientific quality, not just task completion
Bonus qualifications:
- Mammalian cell culture, transfection, or cell-based assays
- Antibody production, protein characterization, or quantitative binding measurements
- Lab automation, liquid handlers, or high-throughput workflows
- Python or R for experimental data analysis and organization
