R&D De Novo Protein Design Intern

Job Objective

This is a split wet-lab/dry-lab role supporting a de novo/scaffold protein engineering campaign to improve the functional attributes of an engineered enzyme. The intern will combine hands-on wet-lab work with AI-driven computational protein design, working collaboratively with a multidisciplinary team of experimental biologists, chemists, and computational scientists to develop innovative protein design platforms leveraging AI.

Core Duties

• Wet lab: support experimental characterization of candidate designs
• Express and purify candidate protein designs
• Run functional assays to evaluate candidate designs
• Apply rational protein design methods (e.g., alanine scanning, site-saturation mutagenesis)
• Dry lab: execute the AI-driven de novo design workflow
• Submit and monitor design jobs
• Analyze predicted structures and sequences
• Collaborate with experimental and computational scientists to integrate computational predictions with laboratory experiments
• Prepare and present research findings, including progress updates and results, to the project team and internal stakeholders
• Document experiments and computational workflows (e.g., lab notebooks, version-controlled code) so results are reproducible and can be shared across the team

Key Qualifications

• Currently pursuing a PhD in Biochemistry, Computational Biology, Molecular Biology, or a related field
• Hands-on wet-lab experience with recombinant protein expression and purification and with biochemical/biophysical assays
• Proficiency in Python for scientific computing, data analysis, and pipeline development
• Familiarity with viewing and analyzing protein structures and sequences
• Strong communication and teamwork skills, with the ability to collaborate effectively across wet-lab and computational disciplines
• Strong analytical and problem-solving skills, with the ability to interpret complex experimental and computational data
• Demonstrated proficiency in digital tools and technology, including Microsoft applications and mobile platforms

Preferred Qualifications

• Experience with AI-driven protein design tools (e.g., ProteinMPNN, RFdiffusion) or structural bioinformatics
• Comfort working in a Linux/command-line environment
• Experience with version control (e.g., Git) for scripts and analysis workflows
• Exposure to chemistry, particularly substrate/analog design and structure-activity relationship (SAR) reasoning