POD Lead – Computational Protein Design Expert

Role overview

We are seeking an experienced computational protein design expert to explore and evaluate advanced generative AI approaches for protein engineering as part of a frontier AI scientific evaluation initiative. The role involves developing scientific evaluation tasks, conducting computational experiments, evaluating AI-generated protein designs, and producing reproducible benchmarks and technical proof-of-concept results.

Responsibilities

• Design rigorous evaluation tasks for de novo protein design, target-conditioned binder generation, motif scaffolding, and sequence–structure optimization.
• Develop and run 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 protocols.
• Evaluate generated sequences and structures for folding confidence, predicted stability, geometric validity, interface quality, novelty, and biological plausibility.
• Analyze protein folding, sequence–structure relationships, and protein–protein interactions.
• Develop scoring rubrics, reference analyses, and computational validation methods; identify model limitations and failure cases.
• Distinguish computational predictions from experimentally validated results and document assumptions and limitations.
• Prepare reproducible benchmarks, technical documentation, and concise reports; contribute to proof-of-concept demonstrations.

Qualifications

• Ongoing or completed Ph.D. in Computational Biology, Structural Biology, Bioinformatics, Biochemistry, Protein Engineering, or a related discipline.
• Hands-on experience in computational protein design, structure prediction, molecular modeling, or protein engineering.
• Strong knowledge of protein folding, structure–function relationships, molecular interactions, and sequence–structure optimization.
• Familiarity with tools such as RFdiffusion, ProteinMPNN, AlphaFold, Rosetta, PyMOL, or ChimeraX.
• Strong Python skills and experience with computational biology workflows and FASTA, PDB, and mmCIF data.
• Ability to independently design experiments, interpret structures and predictions, assess scientific validity, and communicate findings in technical reports.
• Prior team leadership experience.

Preferred qualifications

Experience with generative or diffusion-based protein design, binder design, motif scaffolding, enzyme engineering, protein optimization or docking, computational screening and benchmarking, AI model evaluation, research collaborations, publications, or open-source computational biology work.

Deliverables

• Reproducible computational protein-design evaluation tasks with defined inputs, expected outputs, and objective scoring rubrics.
• Analysis of AI-generated designs covering structural quality, biological plausibility, limitations, and failure cases.
• A concise technical report and proof-of-concept demonstration.

Engagement details

Short-term contractor assignment for three weeks, with an immediate start expected. The role requires at least 40 hours per week and four hours of overlap with PST. Availability of eight hours per day is also stated.

The maximum rate is USD $15–$20 per hour, with the final rate based on experience and feedback. No medical or paid leave is provided. Two positions are available.