Scientist II, Molecular Function New Emeryville, California, United States; On-site

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 protein design. We're hiring a Scientist II to build and own our yeast surface display platform for binder generation. The data you produce will train and evaluate our protein design models, and the binders that emerge will open up new directions for the company. This role sits within the Molecular Function (MF) program, our cross-functional effort spanning ML, Biology, and Bioinformatics. You will be the scientific owner of the yeast display platform: building it hands-on, from library construction through FACS and NGS readout, then setting its direction as it scales with Profluent's design ambitions. As the platform matures, this role will grow towards setting direction for how display and protein engineering evolve at Profluent, including how we build on the most interesting binders the platform uncovers. We are looking for a yeast display expert who is ready to take on that broader scope. Responsibilities Establish and validate a yeast surface display platform for binder generation, including display vector and strain selection, library design and construction, and FACS-based selection and sorting Design and execute yeast display campaigns across binder formats (e.g., de novo miniproteins, VHHs, scFvs, and alternative scaffolds) against a range of targets Take workflows from concept to a robust, repeatable process: define controls, QC, and acceptance criteria, and characterize reproducibility, signal-to-noise, and the relationship between sort-bin enrichment and binding strength Build NGS readouts into selections and deep mutational scanning workflows, converting raw sequencing counts into quantitative binding metrics that ML models can learn from Partner with the ML scientists, and Protein Design scientists to scope campaigns (targets, library composition, readouts) and to define what "model-ready" binding data looks like Work with Bioinformatics on metadata schemas and data ingestion so every dataset is traceable to its parent campaign Qualifications PhD in Molecular Biology, Biochemistry, Protein Engineering, Biophysics, Immunology, or a closely related field, with 3+ years of relevant industry experience (level and compensation will be commensurate with experience) Deep hands-on experience with yeast surface display, including library construction (e.g., homologous recombination, electroporation at scale), induction and expression optimization, and multi-round selection campaigns (required) Proven track record of generating and screening libraries for binder discovery or optimization (naive, immune, synthetic, or designed) Strong flow cytometry and FACS expertise, including gating strategy, sort design, and data interpretation Working knowledge of NGS-based screening workflows (e.g., display library sequencing, deep mutational scanning, amplicon sequencing) and how to turn that data into a quantitative readout Strong molecular biology skills (cloning, mutagenesis, DNA assembly) Demonstrated ability to define and uphold assay quality standards: controls, reproducibility, and signal-to-noise Solid understanding of protein engineering and developability considerations Comfortable working across disciplines: can discuss binding biophysics with biologists and model-guided design with ML scientists Preferences Experience with titration-based affinity measurement by display and sequencing (Tite-Seq–style) or analogous approaches to recovering KD from pooled data Experience with display-based affinity maturation and binder engineering Experience with antibody characterization (e.g., Carterra SPR, Octet BLI) and protein expression/purification Experience designing experiments specifically to generate ML training data (e.g., focused libraries, systematic mutagenesis, library-on-library screens) Experience with additional display platforms (phage, mammalian, ribosome/mRNA) Experience with lab automation (e.g., Hamilton, Tecan) and LIMS/ELN systems (e.g., Benchling) Experience establishing a new platform and transitioning it to routine execution, including training others to run it Prior startup experience or comfort in fast-paced, ambiguous environments What We Offer High-growth opportunity with meaningful impact on the future of protein design Competitive compensation package with equity participation 401(k) with a strong employer match Generous PTO policy and commitment to work-life balance Professional development opportunities in a cutting-edge field at the intersection of AI and biology Profluent Bio, Inc is an equal opportunity employer promoting diversity and inclusion in the workspace. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical conditions, veteran status, sexual orientation, gender (including gender identity and gender expression), sex (which includes pregnancy, childbirth, and breastfeeding), genetic information, taking or requesting statutorily protected leave, or any other basis protected by law. Work Authorization Requirement Applicants must have ongoing work authorization in the United States that does not require employer sponsorship. Sponsorship will not be provided now or at any time in the future for this position. Employment Eligibility Verification Legal authorization to work in the United States is required. In compliance with federal law, all persons hired must verify their identity and work eligibility and complete the required employment verification form upon hire. Hiring Salary Range

$155,000 - $160,000 USD