The work
Create and solve challenging research problems in biology and related life sciences. Use Python to analyze data, build models and simulations, and create data-processing workflows.
Review scientific solutions and code for correctness, clarity, completeness, and sound reasoning. Check that computational results are valid and reproducible, and identify errors in methods, calculations, or code.
- Work may cover medicine and biomedical science, computational biology, neuroscience, molecular and cellular biology, biotechnology, bioinformatics, genetics and genomics, ecology, evolutionary biology, biostatistics, or biological data science.
What it pays and takes
- Pay: $60 to $120 per hour equivalent, paid per task that meets project specifications.
- Workload: Approximately 15 hours per week, with flexible scheduling.
- Location: Fully remote and open to candidates in the eligible countries listed for this role.
- Language: Fluency in English.
- Education and research: A PhD, MD, or master's degree with substantial research experience in a relevant life sciences field, or equivalent accomplishments. Research experience may come from publications, thesis work, computational projects, industry research, or related work.
- Python: Practical experience with scientific computing, data analysis, modeling, or simulation.
- Review skills: Ability to assess scientific solutions and code for correctness and reproducibility.
- Tools: Familiarity with tools such as NumPy, pandas, SciPy, Matplotlib, scikit-learn, Jupyter, or relevant field-specific libraries.
How it works
You apply here in about two minutes and finish on the hiring platform. The next step is a short AI interview (technical roles include a coding challenge). Passing it is how you qualify for paid tasks, so take it soon after you are invited.
The hiring platform's application for this job asks for your resume, your LinkedIn profile, your phone number, your location and answers to 7 screening questions.
About AI training work
AI training work is the human side of building AI: people create and review examples that help models learn. This role uses life sciences expertise and computational work to assess and improve AI systems.