About Turing
Turing is one of the world’s leading AGI infrastructure companies, working with frontier AI labs to accelerate model development through high-quality training data, evaluations, and engineering talent.
Engagement Details
- Commitments Required: At least 6 hours per day and a minimum of 40 hours per week, including a 4-hour overlap with PST.
- Employment Type: Contractor assignment; no medical or paid leave.
- Duration of Contract: 5 weeks. Expected start date is next week.
- Payment Type: $200 per approved task.
Role Overview:
We are seeking experienced Bioinformatics and Computational Biology experts to develop challenging, research-based scientific tasks using real-world biological datasets for a frontier AI training and evaluation initiative.
In this role, you will design complex bioinformatics research tasks involving genomics, transcriptomics, proteomics, metabolomics, and other biological data. Each task will include a real-world dataset, a scientific question with a single objectively verifiable answer, and a reproducible Python validation notebook demonstrating that the correct answer can be derived from the data.
You will work with raw or minimally processed biological datasets, develop scientifically rigorous evaluation tasks, and establish reliable ground truths to assess the research and reasoning capabilities of Large Language Models (LLMs).
What you'll do:
Design challenging, multi-step bioinformatics research tasks using real-world biological datasets, including sequencing, genomics, transcriptomics, proteomics, metabolomics, and structural biology data.
Source raw or minimally processed datasets from public repositories or other authorized sources, ensuring appropriate licensing and the absence of identifiable patient information.
Develop scientific questions with a single, objectively verifiable correct answer, supported by controlled experimental properties or independently validated biological metadata.
Work with datasets involving whole-genome sequencing (WGS), RNA-seq, single-cell RNA-seq, ChIP-seq, DNA methylation, Hi-C, metagenomics, proteomics, or metabolomics.
Create reproducible Python validation notebooks demonstrating that the expected answer is supported by the underlying biological data.
Validate scientific ground truths using reliable evidence such as PCR-confirmed results, known gene knockouts, verified pedigrees, or other independently confirmed biological information.
Review and sanitize dataset file names, headers, and metadata to prevent unintended disclosure of answers.
Develop scientifically challenging tasks that require advanced biological reasoning, computational analysis, and interpretation of complex datasets while maintaining a unique, verifiable answer.
Distinguish objectively verifiable biological facts from uncertain interpretations, statistical associations, or conclusions dependent on analytical assumptions.
Ensure tasks are well-documented, reproducible, scientifically accurate, and aligned with established evaluation guidelines.
Maintain high standards of task quality and productivity while meeting assigned timelines.
Collaborate with researchers, technical teams, and project stakeholders to communicate progress, resolve blockers, and improve task quality.
What we're looking for:
Ph.D. or postdoctoral experience in Bioinformatics, Computational Biology, or a closely related discipline.
Strong hands-on experience analyzing raw or minimally processed biological datasets in at least one major area: WGS, bulk RNA-seq, single-cell RNA-seq, ChIP-seq, DNA methylation, Hi-C, metagenomics, proteomics, or metabolomics.
Strong Python programming proficiency, with experience developing computational biology workflows, data analysis pipelines, and reproducible scientific notebooks.
Solid understanding of bioinformatics, genomics, molecular biology, biological data analysis, and computational research methodologies.
Experience processing, analyzing, and interpreting complex biological datasets using established bioinformatics tools and analytical approaches.
Ability to independently design scientific research questions with unique, objectively verifiable ground truths.
Strong understanding of biological data quality, experimental validation, scientific uncertainty, and the distinction between factual results and interpretive conclusions.
Experience documenting scientific analyses, computational workflows, and validation methods to ensure reproducibility.
Excellent analytical and problem-solving skills, with the ability to identify biological inconsistencies, hidden data artifacts, and potential sources of incorrect conclusions.
Strong written communication skills and the ability to collaborate effectively with interdisciplinary research and technical teams.
Nice to have:
Advanced expertise in one or more biological domains, including Genomics, Genetics, Molecular Biology, Microbiology, Immunology, Cell Biology, or Structural Biology.
Experience with bioinformatics tools such as samtools, bcftools, STAR, Salmon, Scanpy, Seurat, MACS2, or bedtools.
Familiarity with public biological databases and repositories such as GEO, SRA/ENA, ENCODE, PRIDE, MetaboLights, PDB, NCBI, or Ensembl.
Experience developing scientific research problems, evaluation datasets, computational benchmarks, or automated validation workflows.
Familiarity with Large Language Models (LLMs), AI coding agents, scientific AI evaluation, or machine learning systems.
Experience working with independently validated biological datasets, including PCR-confirmed samples, karyotyping results, known pedigrees, or confirmed CRISPR knockouts.
Wet-lab or clinical research experience, particularly in evaluating experimental reliability and biological metadata.
Strong proficiency in R alongside Python for statistical analysis and computational biology workflows.
Publications in recognized biology or computational biology journals, or contributions to open-source bioinformatics tools.
Experience working in fast-paced, interdisciplinary research environments.
Familiarity with collaborative platforms such as Slack and Google Workspace.
Prior experience in AI training, scientific data annotation, benchmark development, or LLM evaluation programs.
