Antibody Engineering & Directed Evolution - Research Scientist

LLNL•Livermore, CA

1d•Onsite

About The Position

We are seeking a dedicated, highly motivated Research Scientist to lead high-impact multidisciplinary research at the interface of 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 lead and champion frontier experimental R&D focused on identification, evaluation, and experimental evolution of protein-protein interactions, specifically antibody-antigen interactions, working with a diverse technical team to pioneer computational methods for in silico protein and antibody design. This role involves direct oversight of wet-lab biologists and automation engineers working to develop, automate, and scale data generation techniques to support powerful, predictive models of protein biology. 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.

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.
Ph.D. in Biochemistry, Bioengineering, Synthetic Biology, Chemical Biology, Chemical Engineering or other relevant field.
Significant experience with yeast display and cell sorting (FACS, MACS, custom microfluidic systems).
Advanced knowledge of protein-protein interaction biology, including antibody engineering principles and biophysical characterization methods.
Advanced in molecular cloning and DNA library construction, sufficient to guide the work of junior team members and support troubleshooting and customization.
Significant experience with NGS workflows as applied to display library analysis.
Advanced literacy in computational biology, e.g. ability to work with data in Python or R, interpret bioinformatics outputs, and engage meaningfully with purely computational colleagues.
Significant experience with supervision or mentorship of junior researchers (e.g. graduate students, postdocs, or research associates), with evidence of positive scientific and professional outcomes.
Advanced communication skills, including a proven ability to convey complex experimental concepts clearly to non-experimentalists as well as non-technical stakeholders.
Ability to operate and lead in a fast-paced, dynamic, team-based environment that prioritizes nimbleness in response to technological developments and new information.

Nice To Haves

Yeast display experience, ideally including work with high-diversity libraries sizes (>105), multi-round selection campaigns, and quantitative affinity binning.
Experience with continuous or semi-continuous directed evolution approaches (e.g., OrthoRep, PACE, or other in vivo hypermutation systems).
Experience engineering antibodies or antibody fragments (e.g. scFv, Fab, VHH) to achieve specific practical design goals.
Experience structuring experimental campaigns to generate large training datasets, with an appreciation for the data quality, coverage, and format requirements critical for model development.
Prior experience translating manual experimental workflows to highly customized automated or semi-automated platform.
Familiarity with relevant automation environments, e.g. liquid handling platforms, robotic cell culture, or custom high-throughput systems, sufficient to support engineers building automated display and selection workflows.
Experience managing or coordinating work across independent teams, especially academic-industry collaborations with distinct norms and expectations surrounding IP, authorship, and timelines.

Responsibilities

Develop high-throughput methods for screening protein-protein interactions to support development of AI/ML predictive models; specifically flow cytometry-based assays to characterize yeast surface display libraries (primarily antibody fragments via FACS and MACS).
Guide the implementation and development of in vivo directed evolution capabilities at LLNL.
Develop and maintain timelines for directed evolution campaigns, mapping dependencies between library construction, selection campaigns, and computational modeling cycles.
Oversee a team of automation engineers translating manual data generation workflows into custom automated environments, defining target success criteria and validation benchmarks.
Serve as the primary point of contact between the AI/ML team and the experimental team, translating between the languages and priorities of both domains to ensure clarity and productive collaboration.
Serve as the primary point of contact with academic collaborators, coordinating meetings, joint experimental activities, data/knowledge transfer, and documentation.
Supervise and mentor technical staff (including postdocs), providing regular feedback and guidance on experimental design, data interpretation, and program strategy.
Present research progress and results to internal leadership and funding sponsors.
Lead the development of manuscripts, technical reports, and patent disclosures arising from the work.
Perform other duties as assigned.