Scientist II, Molecular Function

Profluent • Emeryville, CA

19h • $155,000 - $160,000

About The Position

Profluent is hiring a Scientist II to build and own its yeast surface display platform for binder generation. The role is part of the cross-functional Molecular Function program, spanning machine learning, biology, and bioinformatics. The scientist will build the platform hands-on, from library construction through FACS and NGS readout, and guide its development as it scales to support Profluent’s protein design efforts. Data generated by the platform will train and evaluate protein design models, and discovered binders may open new directions for the company.

As the platform matures, the role will expand toward setting direction for display and protein engineering at Profluent, including how the company builds on promising binders identified by the platform. The position requires close partnership with ML, protein design, and bioinformatics teams.

Requirements

• PhD in Molecular Biology, Biochemistry, Protein Engineering, Biophysics, Immunology, or a closely related field.
• At least 3 years of relevant industry experience.
• Deep hands-on yeast surface display experience, including library construction (such as homologous recombination and electroporation at scale), induction and expression optimization, and multi-round selection campaigns.
• Proven track record of generating and screening libraries for binder discovery or optimization, including naive, immune, synthetic, or designed libraries.
• Strong flow cytometry and FACS expertise, including gating strategy, sort design, and data interpretation.
• Working knowledge of NGS-based screening workflows, such as display library sequencing, deep mutational scanning, or amplicon sequencing, and using the resulting data to produce quantitative readouts.
• Strong molecular biology skills, including cloning, mutagenesis, and DNA assembly.
• Ability to define and uphold assay quality standards, including controls, reproducibility, and signal-to-noise.
• Solid understanding of protein engineering and developability considerations.
• Ability to work across disciplines and discuss binding biophysics with biologists and model-guided design with ML scientists.

Nice To Haves

• Experience with titration-based affinity measurement by display and sequencing, such as Tite-Seq-style approaches, or analogous methods for recovering KD from pooled data.
• Experience with display-based affinity maturation and binder engineering.
• Experience with antibody characterization, such as Carterra SPR or Octet BLI, and protein expression and purification.
• Experience designing experiments to generate ML training data, such as focused libraries, systematic mutagenesis, or library-on-library screens.
• Experience with additional display platforms, such as phage, mammalian, or ribosome/mRNA display.
• Experience with lab automation, such as Hamilton or Tecan, and LIMS/ELN systems, such as 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.

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, including de novo miniproteins, VHHs, scFvs, and alternative scaffolds, against a range of targets.
• Turn workflows into robust, repeatable processes by defining controls, QC, and acceptance criteria, and characterizing 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 for ML models.
• Partner with ML scientists and Protein Design scientists to scope campaigns, including targets, library composition, and readouts, and define what constitutes model-ready binding data.
• Work with Bioinformatics on metadata schemas and data ingestion so every dataset is traceable to its parent campaign.

Benefits

• Competitive compensation package with equity participation.
• 401(k) with a strong employer match.
• Comprehensive health, dental, and vision insurance.
• Generous PTO policy and commitment to work-life balance.
• Professional development opportunities in a cutting-edge field at the intersection of AI and biology.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree