Profluent is the frontier AI lab for biology. Profluent builds powerful foundation models for all of life's molecules, unlocking solutions that transform medicine, agriculture, and beyond. Founded in 2022 and headquartered in Emeryville, CA, Profluent is backed by leading investors including Altimeter Capital, Bezos Expeditions, Spark Capital, Insight Partners, Air Street Capital, AIX Ventures, and Convergent Ventures and has raised over $150M to date.
The Role
Profluent is scaling the discovery engines that supply data for AI-driven antibody and protein design. We're hiring an experienced protein engineer to lead display campaigns and build high-throughput platforms for measuring protein-protein interactions (PPI) at scale.
This is primarily a bench role. You'll run your own campaigns, build and validate assays, and set the controls and QC that turn raw screening output into model-ready datasets, working closely with ML, protein design, automation, and bioinformatics colleagues.
Responsibilities
- Build the founding high-throughput platforms for identifying and measuring protein-protein interactions.
- Lead phage display campaigns end-to-end, from library construction through panning, NGS, and hit validation.
- Design and troubleshoot selection strategies, including antigen selection, library QC, and stringency selections.
- Plan campaigns with ML and protein design scientists (targets, assays, readouts) and define what makes binding data fit for model training and evaluation.
Qualifications
- MS (3 + years industry experience) or PhD in (0- 2 years industry experience), Molecular Biology, Biochemistry, Protein Engineering, Biophysics, Immunology, or a related discipline.
- Deep hands-on experience with phage display and antibody discovery (required).
- Strong molecular biology skills for library construction.
- Solid understanding of NGS-based screening and how to turn sequencing data into quantitative measurements.
- Strong analytical and troubleshooting skills, with careful attention to detail and record-keeping.
Preferences
- Experience in therapeutic antibody optimization: affinity maturation, developability, and format engineering.
- Experience in measuring binding kinetics and affinity by SPR or BLI/Octet, and epitope mapping.
