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Profluent

AI-designed bespoke proteins for genetic therapies

Scientist 2 - Molecular Function

Translational Research & Bench BiologyUpdated on Nov 9
Full-timeEmeryville, CA, USAIn-person

About the Job

Requirements

  • A 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 through homologous recombination and large-scale electroporation, induction and expression optimization, and multi-round selection campaigns.
  • A proven track record of generating and screening libraries for binder discovery or optimization, including naive, immune, synthetic, or designed libraries.
  • Strong flow cytometry and fluorescence-activated cell sorting expertise, including gating strategy, sort design, and data interpretation.
  • Working knowledge of next-generation sequencing-based screening workflows, such as display library sequencing, deep mutational scanning, and amplicon sequencing, and how to turn that data into a quantitative readout.
  • 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 discuss binding biophysics with biologists and model-guided design with machine learning scientists.

Responsibilities

  • Establish and validate a yeast surface display platform for binder generation, including display vector and strain selection, library design and construction, and fluorescence-activated cell sorting-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.
  • Take workflows from concept to a robust, repeatable process by defining controls, quality control, and acceptance criteria, and characterizing reproducibility, signal-to-noise, and the relationship between sort-bin enrichment and binding strength.
  • Build next-generation sequencing readouts into selections and deep mutational scanning workflows, converting raw sequencing counts into quantitative binding metrics for machine learning models.
  • Partner with machine learning scientists and Protein Design scientists to scope campaigns, including targets, library composition, and readouts, and define model-ready binding data.
  • Work with Bioinformatics on metadata schemas and data ingestion so every dataset is traceable to its parent campaign.

Desired Qualifications

  • Experience with titration-based affinity measurement by display and sequencing, such as Tite-Seq, or analogous approaches to recovering KD from pooled data.
  • Experience with display-based affinity maturation and binder engineering.
  • Experience with antibody characterization, such as Carterra SPR and Octet BLI, and protein expression and purification.
  • Experience designing experiments specifically to generate machine learning training data, such as focused libraries, systematic mutagenesis, and library-on-library screens.
  • Experience with additional display platforms, including phage, mammalian, and ribosome/mRNA display.
  • Experience with laboratory automation, such as Hamilton and Tecan, and laboratory information management systems/electronic laboratory notebooks, 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.

About the Company

Profluent

AI-designed bespoke proteins for genetic therapies

Profluent.bio designs custom proteins for genetic medicines using artificial intelligence. Its core offering is bespoke protein solutions tailored to therapeutic needs for pharmaceutical and biotech partners, helping advance treatments for genetic diseases. The product works by applying AI to optimize protein design, effectively decoding the “language of life” to create proteins that extend beyond natural or existing patented options. Partners collaborate on projects, with revenue coming from upfront fees, milestones, and royalties on successful commercialization of therapies that use their proteins. The company differentiates itself by combining AI-driven protein design with a partnership model in the gene editing and synthetic biology space, aiming to deliver more effective and precise treatments while enabling cost-efficient development. The goal is to accelerate genetic medicine by providing optimized proteins that enable new or improved therapies for genetic diseases.

Company size
51-200
Company stage
Late Stage VC
Total funding
$149M
Headquarters
Emeryville, CA
Founded
2022

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