AI training jobs for life scientists

Turn published computational biology papers into coding tasks by removing core code and defining held-out evaluation workloads.

Write realistic life sciences tasks and evaluate AI-generated answers for flawed experimental design, misread data, and outdated claims.

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

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 challenging chemistry problems and develop expert solutions to those chemistry problems.

Create challenging chemistry problems and write expert solutions that accompany each of those problems.

Create challenging chemistry problems and write expert solutions that accompany each of those problems.

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

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

Author research-grade scientific-computing tasks from biology research, each with a Python reference solution and tests.

Author research-grade scientific-computing chemistry tasks with a Python reference solution and tests that check the result.

Design, evaluate, and critique chemistry and computational chemistry problems and reasoning chains to support AI benchmarking.

Design, evaluate, and critique biology and bioinformatics workflows, and analyze biological datasets to support AI benchmarking.

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

Write expert-level problems in computational chemistry, structural biology, and drug discovery and evaluate AI model outputs for scientific accuracy.

Design, evaluate, and critique computational chemistry workflows and assess simulation parameters and software configurations for accuracy and reproducibility.

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 tasks from biology work and judge frontier AI model answers against professional standards.

Build realistic tasks from chemistry work and judge frontier AI model answers 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.

Write benign, dual-use, and adversarial chemistry prompts and evaluate AI responses against a defined policy standard.

Write benign, dual-use, and adversarial chemistry prompts, then evaluate AI model responses against a defined policy standard.

Write benign, dual-use, and adversarial chemistry prompts, evaluate model responses against a policy standard, and write reference answers.

Write benign, dual-use, and adversarial chemical safety prompts, then evaluate model responses against a policy standard and write reference answers.

Write single-turn prompts labelled benign, dual-use, or adversarial and evaluate AI responses against a defined policy standard.

Design hard molecular biology tasks with documented solutions and write rubrics used to judge sequence design quality.

Design molecular biology tasks and write documented solutions for primers, plasmids, gRNA, and mRNA constructs that serve as ground truth.

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

Write challenging prompts in your domain and evaluate AI model responses against a defined policy standard.

Write benign, dual-use, and adversarial chemistry prompts and evaluate model responses against a defined policy standard.

Author realistic biology scenarios and grade model output for mechanistic accuracy and evidence handling.

Author molecular biology scenarios with reference answers and grade model output for pathway accuracy and experimental logic.

Build life sciences tasks and judge frontier AI model answers against the standard of your field.

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.

Complete model development, pipeline construction, and research tasks on genomic, transcriptomic, or multi-omics datasets.

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

Create challenging chemistry prompts based on real-world chemical principles and assess model responses.