Computational Biologist, Cheminformatics for AI Training

Apply deep computational biology and cheminformatics expertise to real-world small-molecule drug discovery problems and help train and evaluate next-generation AI systems. This remote contractor role focuses on dataset curation, scientific evaluation, creation of reproducible code-based benchmark tasks, and delivery of detailed scientific feedback that improves model performance and practical relevance. No prior AI experience is required, domain knowledge is the priority.

• Analyze and interpret small-molecule and drug discovery datasets using advanced computational biology, bioinformatics, and cheminformatics methods.
• Curate, annotate, and validate chemical and biological datasets, for example ChEMBL, PubChem, and DrugBank, to support AI-driven discovery workflows.
• Evaluate compound-target interactions, ADMET properties, and lead optimization strategies by integrating chemical, biological, and clinical data sources.
• Provide expert insight on structure-activity relationships and structure-property relationships, medicinal chemistry approaches, and experimental design considerations.
• Build and implement code-based benchmark tasks, for example terminal or CLI-based environments, that reflect realistic computational drug discovery scenarios.
• Develop reproducible computational environments using tools such as Docker and create automated testing pipelines to ensure task correctness and solvability.
• Assess and review AI-generated outputs for scientific rigor, accuracy, and practical relevance, delivering detailed written feedback and recommendations.

• Advanced expertise in computational biology, cheminformatics, medicinal chemistry, biochemistry, or a closely related field. An advanced degree such as PhD, MSc, or PharmD is highly valued but not required.
• Required domain skills include bioinformatics, biochemistry, ADMET, cheminformatics, molecular docking, and structure-activity relationship analysis.
• Strong programming ability in Python beyond one-off analysis scripts, with experience building tools, pipelines, or testable code.
• Familiarity with version control and development workflows, specifically Git and GitHub.
• Experience with containerization and reproducible environments, for example Docker, and building automated tests for computational tasks.
• Practical experience with cheminformatics toolkits and platforms such as RDKit, KNIME, Schrödinger, OpenEye, MOE, or similar.
• Proven track record in small-molecule drug discovery activities, including SAR/QSAR evaluation, ADMET prediction, or virtual screening workflows.
• Comfort working with public chemical and bioactivity databases and integrating diverse datasets for scientific analysis.
• Ability to produce clear, detailed written feedback and reports that communicate complex chemical and biological concepts.
• Experience in multidisciplinary and remote projects is preferred. Familiarity with AI-assisted coding tools is a plus. No prior AI-specific experience is required.

• Engagement type: Independent contractor.
• Location: Remote.
• Work involves contributing subject matter expertise to computational drug discovery projects, creating benchmark tasks and reproducible environments, and evaluating AI outputs.

• Hourly rate: 80 to 110 USD per hour.

• Candidates must be able to engage as independent contractors and perform remote contract work.