Antibody Engineering & Directed Evolution - Postdoctoral Researcher

LLNL•Livermore, CA

6d•Onsite

About The Position

We are seeking a dedicated, highly motivated Postdoctoral Researcher to conduct high-impact multidisciplinary research at the interface between protein design, ultra-high-throughput experimentation, and directed evolution. The project you will join spans multiple teams in the Physical and Life Sciences Directorate, Computing, and Engineering, working on state of the art approaches for antibody and protein design and optimization. We have extensive collaborations within the University of California network, and experts at Universities, National Laboratories, and industry. You will actively develop and deploy protein display-based technologies for identifying, evaluating, and evolving protein-protein interactions, particularly those involving antibodies. You will interact extensively with AI/ML scientists and automation engineers to develop predictive models for binder design and, ultimately, to automate and scale the data generation process. This position is in the Synthetic Biology Group in the Biosciences and Biotechnology Division within the Physical and Life Sciences Directorate. This position requires full-time on-site presence due to the nature of the work. NOTE: This is a two-year Postdoctoral appointment with the possibility of extension to a maximum of three years.

Requirements

Must be eligible to access the Laboratory in compliance with Section 3112 of the National Defense Authorization Act (NDAA). See Additional Information section below for details.
PhD in Biochemistry, Bioengineering, Synthetic Biology, Chemical Biology, Chemical Engineering or other relevant field with a strong emphasis in molecular microbiology or protein biology.
Experience involving protein engineering, protein-protein interactions, antibody biology, or a closely related experimental discipline.
Practical experience with flow cytometry and FACS-based cell sorting, including proficiency with relevant software (e.g. FlowJo or equivalent).
Significant experience with molecular cloning, including Gibson and Golden Gate assembly, library-scale assembly, vector design, mutagenesis, and NGS-based verification.
Competency in basic yeast genetics and cultivation (familiarity with S. cerevisiae as a model organism is expected).
Working knowledge of how protein-protein interactions are evaluated, including characterization methods (e.g., flow cytometry-based titrations, BLI, SPR, ELISA).
Familiarity with standard statistical analysis of experimental data and proficiency in at least one data analysis environment (e.g. Python, R, MATLAB).
Proficient verbal and written communication and interpersonal skills to collaborate effectively in a multidisciplinary team environment and successfully communicate technical information.
Demonstrated ability to work and troubleshoot independently while also functioning as a collaborative team member in an interdisciplinary setting.

Nice To Haves

Direct, extensive experience with yeast display, ideally including work with high-diversity libraries sizes (>105), multi-round selection campaigns, and quantitative affinity binning.
Experience constructing combinatorial or DMS/SSM genetic libraries.
Hands-on experience with MACS for qualitative enrichment of display libraries, including bead coupling of biotinylated antigens and optimization of selection stringency.
Exposure to continuous or semi-continuous directed evolution approaches (e.g., OrthoRep, PACE, or other in vivo hypermutation systems).
Demonstrated experience engineering antibodies or antibody fragments (e.g. scFv, Fab, VHH).
Experience with NGS-based library characterization, ideally analying deep sequencing data from sorted yeast pools to track enrichment across selection rounds.
Comfort with automation and liquid handling platforms (e.g., Opentrons, Hamilton, Tecan, Beckman), or minimally, experience working collaboratively to adapt benchtop protocols to automated workflows.
Familiarity with AI-based protein design concepts and tools, e.g., RFdiffusion, ProteinMPNN, AlphaFold, or antibody-specific models.

Responsibilities

Design, construct, and characterize yeast surface display libraries (primarily antibody fragments) using FACS and MACS.
Develop and optimize flow-cytometry binding assays (titrations/affinity binning) and complementary characterization (e.g., ELISA, BLI, SPR).
Implement and adapt directed-evolution workflows (e.g., in vivo diversification, iterative selection) in collaboration with the project team.
Collaborate with automation engineers to transfer manual display and selection protocols onto liquid handling and robotic platforms.
Collaborate with machine learning experts on designing antibody libraries, experimental data analysis, and interpretation.
Present research progress and results clearly and regularly to interdisciplinary team members, including scientists without wet-lab backgrounds.
Contribute to manuscript preparation, internal technical reports, and (as appropriate) patent disclosures arising from the work.
Perform other duties as assigned.