Scientist II, Molecular Function
AI-first protein-design company using generative foundation models and wet-lab validation to create functional proteins and genome-editing systems.
Funding history
About Profluent
Profluent develops AI models for de novo protein design, with applications in biomedicine, agriculture, and industrial biology. Its platform includes protein-generation models such as ProGen3 and the AI-designed OpenCRISPR-1 gene editor.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will build and own Profluent's yeast surface display platform for binder generation, working hands-on from library construction through FACS and NGS readout. You will design and run campaigns across binder formats and targets, turn workflows into robust repeatable processes with clear controls and QC, and convert sequencing counts into quantitative binding metrics that ML models can learn from. You will partner with ML, protein design and bioinformatics colleagues, and as the platform matures you will help set direction for display and protein engineering.
Requirements
- PhD in Molecular Biology, Biochemistry, Protein Engineering, Biophysics, Immunology, or a closely related field, with 3+ years of relevant industry experience
- Deep hands-on experience with yeast surface display, including library construction, induction and expression optimization, and multi-round selection campaigns
- Proven track record of generating and screening libraries for binder discovery or optimization
- Strong flow cytometry and FACS expertise, including gating strategy, sort design, and data interpretation
- Working knowledge of NGS-based screening workflows and turning data into quantitative readouts
- Strong molecular biology skills (cloning, mutagenesis, DNA assembly)
- Ability to define and uphold assay quality standards
- Solid understanding of protein engineering and developability considerations
- Comfortable working across disciplines with biologists and ML scientists
- Ongoing US work authorization without employer sponsorship
Responsibilities
- Establish and validate a yeast surface display platform for binder generation, including vector and strain selection, library design and construction, and FACS-based selection and sorting
- Design and execute yeast display campaigns across binder formats (de novo miniproteins, VHHs, scFvs, alternative scaffolds) against a range of targets
- Take workflows from concept to robust, repeatable processes by defining controls, QC and acceptance criteria and characterizing reproducibility, signal-to-noise and enrichment-to-binding relationships
- Build NGS readouts into selections and deep mutational scanning workflows, converting sequencing counts into quantitative binding metrics for ML models
- Partner with ML and Protein Design scientists to scope campaigns and define model-ready binding data
- Work with Bioinformatics on metadata schemas and data ingestion so every dataset is traceable to its parent campaign
Benefits
- Equity participation
- 401(k) with strong employer match
- Health, dental and vision insurance
- Generous PTO policy