Research Associate (Biology)

Anthrogen•San Francisco, CA

17h•Onsite

About The Position

Anthrogen is seeking a Research Associate to support experimental validation of designed proteins. The role focuses on making and characterizing proteins, with ownership of experimental workflows from construct preparation through sample quality control and assay execution. The Research Associate will work closely with Platform scientists, learn methods, and improve experimental workflows. The position is based onsite in San Francisco and is full-time.

Requirements

Excellent hands-on molecular biology or protein biochemistry skills demonstrated through substantial bench work.
Ability to independently run familiar workflows, recognize common failure modes, and troubleshoot systematically.
Careful handling of samples, controls, calculations, and records.
Ability to explain how an experiment's success was determined.
Ability to learn quickly, communicate clearly, and ask for help before avoidable errors result in unusable work.
Interest in taking responsibility for the quality of the science, not just completing assigned tasks.
A BS/MS in a relevant field or equivalent practical experience is described as a useful foundation.

Nice To Haves

Mammalian cell culture, transfection, or cell-based assay experience.
Experience with antibody production, protein characterization, or quantitative binding measurements.
Experience with lab automation, liquid handlers, or high-throughput workflows.
Python or R experience for analyzing and organizing experimental data.

Responsibilities

Execute molecular biology workflows, including cloning, PCR, transformations, and construct verification, while maintaining strong 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 work with scientists to develop and troubleshoot new assays.
Maintain clear experimental records, perform initial data analysis, and flag unexpected results or quality problems early.
Improve protocols and day-to-day lab workflows while preserving data reliability as throughput increases.