AI training jobs for life scientists

Create computational life-science tasks that challenge advanced AI models and evaluate model-generated code, analyses, models, and experiments.

Design, evaluate and critique bioinformatics workflows and assess pipeline parameters and tool configurations for accuracy and reproducibility.

Curate, annotate, and validate drug discovery datasets, and assess AI-generated outputs for scientific accuracy, relevance, and reliability.

Write expert-level computational chemistry problems with verifiable solutions and review AI model outputs for scientific accuracy.

Write expert-level life sciences problems with verifiable solutions and review AI model outputs for scientific accuracy.

Evaluate biology and bioinformatics workflows and analyze biological datasets to provide recommendations for AI benchmarking initiatives.

Evaluate chemistry and computational chemistry workflows and analyze spectroscopic and simulation data to support AI benchmarking.

Develop reproducible Python-based analyses and review scientific solutions and code for correctness, clarity, and completeness.

Review life sciences reasoning tasks and model outputs, and write problem specifications and reference solutions.

Assess chemistry tasks and model outputs for errors, and write precise problem statements and rigorous reference solutions.

Write realistic chemistry problems with expert answers and evaluate AI-generated answers for wrong mechanisms, calculation errors, and unsafe procedures.

Evaluate AI model outputs on biology tasks and design benchmarks and training data for biology problems.

Write realistic life sciences tasks and evaluate AI-generated answers for flawed experimental design, misread data, and outdated claims.

Evaluate AI model outputs on drug discovery tasks and design benchmarks and training data for drug discovery problems.

Evaluate AI model outputs on chemistry tasks and design benchmarks and training data for chemistry problems.

Create and solve technically rigorous scientific problems and develop reproducible Python-based analyses, models, simulations, or data-processing workflows.

Create, evaluate, and refine AI-generated environmental impact assessment reports and water and soil quality monitoring data sheets.

Create, evaluate, and refine AI-generated environmental impact assessment reports and species habitat survey summaries.

Create self-contained problems involving biomedical signals and time-series data, and develop reference solutions using Python or MATLAB.

Create, evaluate, and refine AI-generated presentations and slide decks, including scientific presentation decks, KOL engagement slides, and advisory board materials.

Create, evaluate, and refine AI-generated presentations and slide decks, including scientific presentation decks and KOL engagement slides.

Create, evaluate, and refine AI-generated presentations and slide decks, including scientific presentation decks and KOL engagement slides.

Build realistic biology tasks, give them to frontier AI models, and judge the results.

Build realistic tasks from your chemistry work, run them through frontier AI models, and judge the results against professional standards.

Create, evaluate, and refine AI-generated scientific presentations and slide decks, such as KOL engagement slides and congress data readout presentations.

Create, evaluate, and refine AI-generated presentations such as conference decks, research posters, and grant proposal figures.

Write biotech development problems with reference answers and grade model-written process and assay development work.

Write synthesis problems with reference answers and grade model-proposed routes and mechanisms for AI evaluation.

Write biology research problems with reference answers and grade model-written experimental designs and interpretations.

Review AI-generated biochemistry content for scientific accuracy and write high-quality rewrites and model answers.

Build realistic life sciences tasks from your own work and judge frontier AI model answers.

Review AI-generated scientific reasoning and rate and rank model outputs against scientific criteria in ALD.

Review AI-generated neuroscience/cognitive science content against project guidelines and provide written feedback to trainers.

Review AI-generated chemistry content and trainer work against project guidelines, giving precise written feedback on scientific accuracy.

Review AI-generated biology content and trainer/QA work against project guidelines, providing precise written feedback.

Review AI-generated chemistry responses, identify calculation or methodology errors, and rate multiple responses by chemical correctness.

Review AI-generated responses, evaluate reasoning quality and step-by-step problem-solving, and provide expert feedback on biological accuracy.

Train and evaluate AI models in Biology and create tasks and deliverables based on real-world scenarios.