Postdoctoral Fellow: AI-Driven Enzyme Discovery & Engineering

University of California - Davis; Davis (CA); On-site; USD 71,000 - 86,000; Full time

Job summary

The Siegel Lab at University of California, Davis is seeking a Postdoctoral Scholar to lead discovery, engineering, and characterization of enzymes for next-generation metabolic health technologies. The project combines rational design, AI-guided engineering, directed evolution, genomic mining, and high-throughput screening to optimize enzyme variants.

The successful candidate will design enzyme campaigns, including library design, expression, purification, characterization, and kinetics.

Qualifications

• PhD in Biochemistry, Protein Engineering, Molecular Biology, Synthetic Biology, Chemical Biology, Biomedical Engineering, or a related field.
• Demonstrated experience in protein expression, purification, biochemical characterization, and enzyme engineering.
• Experience with high-throughput cloning, library generation, directed evolution, or enzyme screening platforms.
• Familiarity with computational approaches for protein engineering, sequence analysis, or structural biology.
• Experience with AI-based structure prediction and design tools (AlphaFold, Chai, Boltz, RFdiffusion, ProteinMPNN) and data science workflows for directed evolution.
• Excellent written and verbal communication skills, with the ability to manage multiple research projects and work effective in a collaborative environment.

Responsibilities

• Enzyme Discovery and Library Development: Develop enzyme discovery pipelines integrating rational design, AI-guided protein engineering, genomic mining, and directed evolution.
• High-Throughput Screening: Develop and optimize plate based high-throughput screening assays to evaluate enzyme activity, specificity and stability.
• Protein Engineering: Lead iterative enzyme engineering campaigns to improve catalytic efficiency, substrate specificity, stability, and overall performance.
• Protein Production and Characterization: Express, purify and characterize lead enzyme variants.
• Data Analysis and Collaboration: Analyze experimental data, document findings, and work closely with an industry partner to guide iterative enzyme optimization and technology development.
• Research Leadership: Mentor researchers, contribute to manuscripts and grant proposals. Support a collaborative laboratory environment.

Skills

Protein expression

Protein purification

Biochemical characterization

Enzyme engineering

High-throughput screening

AI-assisted design

Data analysis

Scientific writing

Education

PhD in Biochemistry or related field

Tools

AlphaFold

Boltz

RFdiffusion

ProteinMPNN

Chai

Job description

The Siegel Lab at University of California, Davis is seeking a Postdoctoral Scholar to lead discovery, engineering, and characterization of enzymes for next-generation metabolic health technologies. The project combines rational design, AI-guided engineering, directed evolution, genomic mining, and high-throughput screening to optimize enzyme variants.

The successful candidate will design enzyme campaigns, including library design, expression, purification, characterization, and kinetics.