Biology Expert (Task Based)
- In this role, you will collaborate on AI-focused projects involving reasoning, problem-solving, data interpretation, and scientific explanation development. You will also contribute to building evaluation benchmarks for advanced Biology topics ranging from undergraduate to PhD-level concepts.Responsibilities
- Design and solve challenging Biology problems for AI model evaluation
- Create clear, step-by-step solutions with strong scientific reasoning
- Collaborate with researchers to improve AI model performance
- Contribute to benchmark creation for Biology curricula
- Analyze areas where language models struggle, including abstraction and multi-step reasoning
- Provide detailed annotations and constructive feedback
- Required Qualifications
- Master’s degree, Ph.D., Postdoctoral degree, or currently pursuing advanced studies in:
- Biology
- Biotechnology
- Biochemistry
- Related fields
- Strong research and analytical skills
- Excellent English comprehension and communication skills
- Ability to work independently in a remote environment
- Reliable desktop/laptop and internet connection
- Availability for 40 hours/week with at least 4 hours PST overlap
Technical Requirements
Candidates should be proficient in at least three of the following tools:
- BWA, Bowtie2, GATK, HMMER, HHsuite, STAR, SPAdes, IQ-TREE, OpenMM, AlphaFold2, PyOpenMS, RELION, PhysiCell, COPASI, Comet, COBRApy, Hifiasm, ABySS, DeepVariant, Flye, OR-Tools, Bracken, Kraken2, MEGAHIT, antiSMASH, BEAST2, FastTree, DIA-NN, Percolator, AutoDock Vina, CP2K, Salmon, MethylDackel, Canu, and GROMACS.Additional Notes
- LinkedIn profile must be included in the resume
- Candidates will undergo a background verification after onboarding
- Work allocation begins only after successful verification
- This is a freelance/contractor engagement with no medical or paid leave benefits
- Perks
- Fully remote work setup
- Opportunity to work on cutting-edge AI projects
- Collaborate with leading LLM research teams
- Potential contract extension based on performance
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