Computational Protein Design 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

We are seeking experienced Computational Protein Design experts to explore and evaluate advanced generative AI approaches for protein engineering as part of a frontier AI scientific evaluation initiative. In this role, you will develop challenging scientific evaluation tasks involving de novo protein generation, diffusion-based structure design, protein binder development, and sequence–structure optimization. You will independently conduct computational experiments, evaluate AI-generated protein designs, and develop reproducible benchmarks and technical proof-of-concept results to assess the capabilities and limitations of generative AI models in protein engineering. Design scientifically rigorous evaluation tasks involving de novo protein design, target-conditioned binder generation, motif scaffolding, and sequence–structure optimization. Develop and execute computational protein design workflows using generative AI models, diffusion-based approaches, and established protein engineering tools. Define task inputs, biological constraints, expected outputs, evaluation criteria, and reproducible experimental protocols. Evaluate AI-generated protein sequences and structures for folding confidence, structural stability predictions, geometric validity, interface quality, novelty, and biological plausibility. Analyze protein folding, sequence–structure relationships, and protein–protein interactions to assess the quality and feasibility of generated designs. Develop objective scoring rubrics, reference analyses, and computational validation methods to evaluate model performance. Identify model limitations, failure cases, unrealistic structural predictions, and potential weaknesses in generated protein designs. Distinguish computational predictions from experimentally validated results and document scientific assumptions and limitations.

Required skills:

• Python Ph.D. (ongoing or completed) in Computational Biology, Structural Biology, Bioinformatics, Biochemistry, Protein Engineering, or a closely related discipline.
• Hands-on experience in at least one major area: computational protein design, protein structure prediction, molecular modeling, or protein engineering.
• Strong understanding of protein folding, protein structure–function relationships, molecular interactions, and sequence–structure optimization.
• Practical familiarity with computational protein design and structural analysis tools such as RFdiffusion, ProteinMPNN, AlphaFold, Rosetta, PyMOL, or ChimeraX.
• Strong programming skills in Python, with experience developing or executing computational biology workflows.
• Experience working with protein sequence and structural data formats such as FASTA, PDB, and mmCIF.
• Ability to evaluate protein structures, interpret computational predictions, and assess the scientific validity of generated designs.