Computational Protein Design & AI Evaluation Expert

Turing; United States; Remote; USD 120,000 - 180,000; Full time

Job summary

Turing is seeking experienced Computational Protein Design experts to explore generative AI approaches for protein engineering. You will design challenging evaluation tasks, run computational experiments, and develop reproducible benchmarks to assess AI models in protein design.

Your work will involve diffusion-based design, binder generation, and sequence–structure optimization, with emphasis on rigor and scientific validity.

Qualifications

• PhD in Computational Biology, Structural Biology, Bioinformatics, Biochemistry, Protein Engineering, or close field.
• Hands-on experience in computational protein design, structure prediction, molecular modeling, or protein engineering.
• Strong understanding of protein folding, structure–function relationships, molecular interactions and sequence–structure optimization.
• Experience with design tools like RFdiffusion, ProteinMPNN, AlphaFold, Rosetta, PyMOL, ChimeraX.

Responsibilities

• Develop evaluation tasks for de novo protein generation and structure design.
• Independently run computational experiments and assess AI-generated designs.
• Create reproducible benchmarks, protocols and proof-of-concept results.
• Evaluate model predictions for folding confidence, stability, geometry, interfaces, novelty, and plausibility.

Skills

Python

Computational design

Protein structure

Molecular modeling

Workflow development

FASTA/PDB/mmCIF

data interpretation

Education

PhD in Computational Biology

Tools

RFdiffusion

ProteinMPNN

AlphaFold

Rosetta

PyMOL

ChimeraX

Job description

Turing is seeking experienced Computational Protein Design experts to explore generative AI approaches for protein engineering. You will design challenging evaluation tasks, run computational experiments, and develop reproducible benchmarks to assess AI models in protein design.

Your work will involve diffusion-based design, binder generation, and sequence–structure optimization, with emphasis on rigor and scientific validity.