Computational Protein Design Expert

You will develop and evaluate scientific tasks for generative AI protein engineering. Your work will cover de novo protein generation, diffusion-based structure design, target-conditioned binder development, motif scaffolding, and sequence-structure optimization.

You will assess what generative models can and cannot do using reproducible computational experiments. You will distinguish computational predictions from results that have been validated experimentally.

• Design protein design evaluation tasks with defined inputs, biological constraints, expected outputs, evaluation criteria, and reproducible protocols.
• Develop and run computational workflows using generative AI models, diffusion-based methods, and protein engineering tools.
• Evaluate generated protein sequences and structures for folding confidence, structural stability, geometric validity, interface quality, novelty, and biological plausibility.
• Analyze protein folding, sequence-structure relationships, molecular interactions, and protein-protein interfaces.
• Create scoring rubrics, reference analyses, validation methods, benchmarks, and technical documentation.
• Identify model limitations, failure cases, unrealistic structural predictions, and weaknesses in generated designs.
• Prepare technical reports and proof-of-concept demonstrations.

This is an entry-level, part-time contract role focused on computational protein design evaluation. The role requires at least 20 hours per week and is open to candidates in the listed countries.

• Pay: No pay rate is provided.
• Hours: 20+ hours per week.
• Work arrangement: Part-time contract work.
• Location: Open to candidates in India, Pakistan, Nigeria, Kenya, Egypt, Ghana, Bangladesh, Turkey, and Mexico.
• Education: An ongoing or completed Ph.D. in computational biology, structural biology, bioinformatics, biochemistry, protein engineering, or a closely related field.
• Core experience: Hands-on experience in computational protein design, protein structure prediction, molecular modeling, or protein engineering.
• Technical skills: Strong Python programming and experience developing or running computational biology workflows.
• Scientific skills: Knowledge of protein folding, structure-function relationships, molecular interactions, and sequence-structure optimization.
• Evaluation skills: Ability to assess protein structures and computational predictions, judge scientific validity, identify technical limitations, and explain findings clearly in technical reports.

Experience with RFdiffusion, ProteinMPNN, AlphaFold, Rosetta, PyMOL, or ChimeraX is valuable. Familiarity with FASTA, PDB, and mmCIF sequence or structural data formats is also useful.

Additional relevant experience includes de novo protein design, binder design, motif scaffolding, enzyme engineering, protein-protein docking, computational affinity assessment, scientific evaluation tasks, scoring rubrics, reference solutions, automated evaluation pipelines, and reproducible computational benchmarks.

Research contributions, publications, open-source development, AI training, or scientific model evaluation experience are also helpful.

Apply on OpenTrain with your resume, then complete the application on the hiring site.

AI training work uses human expertise to create and review examples that help artificial intelligence systems improve. In specialized projects like this one, people with scientific knowledge evaluate model outputs, define reliable tests, and identify where predictions are useful or unreliable.