Computational Protein Design Expert

OpenTrain AI, Inc.; India; A distancia; INR 827.000 - 1.378.000; A tiempo parcial

Descripción de la vacante

OpenTrain AI, Inc. seeks an entry-level contractor in a remote, part-time role to advance generative AI protein design. You will work on de novo generation, diffusion-based structure design, and sequence-structure optimization using Python and established tools.

Expect evaluation of predictions vs. experimental results and documentation of methods and benchmarks. The position emphasizes strong scientific judgment, programming ability, and clear technical communication.

Formación

• Ph.D. in computational biology or related field is required or in progress.
• Strong Python programming and ability to develop/run computational biology workflows.
• Excellent written and spoken English for technical reporting.

Responsabilidades

• Design and evaluate generative AI protein engineering tasks with defined inputs and scoring criteria.
• Build and run workflows with AI models and protein design tools.
• Evaluate protein sequences/structures for folding, stability, and plausibility.
• Analyze sequence-structure relationships and protein interfaces.
• Produce technical reports, benchmarks, and demonstrations.

Conocimientos

Python programming

Computational biology

Bioinformatics

Scientific reporting

Educación

Ph.D. in computational biology or related field

Herramientas

RFdiffusion

ProteinMPNN

AlphaFold

Rosetta

PyMOL

ChimeraX

FASTA

PDB

mmCIF

Descripción del empleo

Evaluate generative AI protein designs using computational biology, protein engineering, and Python. This contract role offers part-time work of 20 or more hours per week across nine countries.

Remote

Contract, part-time

Engagement

Remote

Location

Oct 29, 2026

Posted

Open to applicants in

The work

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 run reproducible computational experiments, assess model capabilities and limitations, and distinguish computational predictions from experimentally validated results.

• Design protein design evaluation tasks with defined inputs, biological constraints, expected outputs, scoring criteria, and reproducible protocols.
• Build and run workflows with generative AI models, diffusion-based methods, and protein engineering tools.
• Evaluate protein sequences and structures for folding confidence, 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 structure predictions, and weaknesses in generated designs.
• Prepare technical reports and proof-of-concept demonstrations.

What it pays and takes

This is an entry-level, part-time contractor role for candidates with advanced training in computational biology or a related field. The work requires strong scientific judgment, programming ability, and clear technical communication.

• Pay: No rate is provided for this role.
• Time: 20 or more hours per week.
• Location: Open to candidates in India, Pakistan, Nigeria, Kenya, Egypt, Ghana, Bangladesh, Turkey, and Mexico.
• Language: Professional fluency in English.
• Education: An ongoing or completed Ph.D. in computational biology, structural biology, bioinformatics, biochemistry, protein engineering, or a closely related discipline.
• Technical skills: Strong Python programming and experience developing or running computational biology workflows.
• Scientific skills: Evaluate protein structures, interpret computational predictions, assess scientific validity, identify technical limitations, and communicate findings in technical reports.
• Helpful tools and formats: RFdiffusion, ProteinMPNN, AlphaFold, Rosetta, PyMOL, ChimeraX, FASTA, PDB, and mmCIF.
• Helpful experience: De novo protein design, binder design, motif scaffolding, enzyme engineering, protein-protein docking, computational affinity assessment, evaluation tasks, scoring rubrics, reference solutions, automated evaluation pipelines, reproducible benchmarks, research publications, open-source development, AI training, or scientific model evaluation.

About AI training work

AI training is the human work behind systems that learn from examples, including reviewing model outputs and evaluating scientific predictions. People with specialized knowledge help define reliable tasks, judge accuracy, and identify failures in complex model behavior.

Requirements

• Experience: Entry level