Computational Protein Research Associate (Contract)

Zymo Research Corporation•Tustin, CA

2d•$75,000 - $85,000•Onsite

About The Position

Zymo Research is seeking a highly motivated and innovative Computational Protein Research Associate to join our multidisciplinary research team focused on protein engineering, enzyme discovery, antibody design, and AI-driven molecular modeling. The successful candidate will work at the intersection of computational biology, machine learning, structural biology, and protein engineering. Key responsibilities will include developing scalable protein sequence–structure–function analysis platforms, building predictive AI/ML models, and identifying or designing proteins with desired biological properties. This role offers the opportunity to contribute to cutting-edge platform technologies in enzymes, antibodies, synthetic biology, and molecular diagnostics. Join us in making a meaningful impact!

Requirements

Bachelor’s degree or higher in computational biology, bioinformatics, structural biology, computer science, biophysics, protein engineering, or related field.
Strong foundation in computational protein science, or structural biology.
Experience applying machine-learning or deep-learning methods to biological sequence, structure, or functional data.
Proficiency in Python and commonly used scientific computing and machine-learning libraries.
Strong analytical, problem-solving, and communication skills.
Ability to work independently while contributing effectively within a multidisciplinary research environment.

Nice To Haves

Experience with protein language models, structure-based modeling, or molecular simulation is highly desirable.

Responsibilities

Apply protein language models and other machine-learning approaches, including model fine-tuning, to analyze protein sequences, predict molecular properties, and annotate protein functions.
Use structural modeling and simulation tools, including AlphaFold 3, Rosetta, and molecular dynamics simulations, to investigate protein structures, conformational stability, molecular interactions, and functional mechanisms.
Apply AI/ML methods to de novo protein design, protein optimization, enzyme engineering, antibody design, and functional prediction.
Perform computational screening and prioritization of candidate proteins for experimental validation.
Collaborate closely with wet-lab scientists to design experiments, interpret results, and iteratively improve engineered proteins.
Contribute to active-learning-driven Design–Build–Test–Learn cycles for protein engineering and optimization.
Present research findings internally and externally through reports, publications, and scientific conferences.
Contribute to intellectual property generation and platform development.