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Remote Pod Lead Life Science

Turing

Job Location:

Bengaluru - India

Hourly Salary: INR 18 - 20

Posted: 28 October 2026 (5 days ago)

Application Deadline: 25 January 2027

Vacancies: 1 Vacancy

Job Summary

About Turing:

Turing is one of the worlds 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 with a 4-hour overlap with PST.
Employment Type: Contractor assignment (no medical/paid leave)
Duration of Contract: 5 weeks (expected start date is next week)

About the Role:

We are seeking an experienced Life Sciences or Medicine expert to lead a pod of trainers building realistic terminal-based scientific and technical tasks for Terminal Bench Science. You will review every task your trainers produce for scientific correctness reproducibility data compliance and grader reliability and coach trainers to consistently deliver high-quality challenging tasks.

Key Responsibilities:

Review trainer tasks end to end including task statements biological and clinical datasets reference files pipelines environments reference solutions and automated tests.
Verify that tasks reflect authentic research or clinical analysis workflows and require genuine multi-step scientific reasoning.
Check that tools reference genomes annotation versions databases and dependencies are pinned and that pipelines are reproducible.
Validate scientific correctness including genome builds coordinate conventions normalization batch-effect handling statistical assumptions multiple-testing correction units dosing conventions imaging orientation and spacing and random-seed control.
Ensure graders verify substantive outputs such as variant calls differential expression results cluster assignments binding or structural metrics survival estimates model performance segmentation accuracy and pharmacokinetic parameters rather than formatting.
Ensure tolerances account for legitimate stochastic and numerical variation without accepting incorrect analyses.
Ensure tasks cannot be gamed through hard-coded outputs reading test files or bypassing the intended analysis.
Confirm that all data is synthetic public appropriately licensed and de-identified with no protected health information or restricted-access data.
Screen tasks for biosafety and dual-use concerns.
Escalate content involving dangerous pathogens toxins or enhancement of harmful biological capabilities.
Provide clear actionable written feedback and track revisions through closure.
Onboard and mentor trainers.
Allocate work track quality and throughput and escalate blockers.
Maintain pod documentation covering guidelines recurring issues and exemplary tasks.
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Required Qualifications:

Ph.D. M.D. M.D.-Ph.D. postdoctoral experience or equivalent advanced experience in a life science biomedical or medical field with a substantial computational component.
Strong programming skills in Python or R plus Bash with Linux and terminal proficiency.
Hands-on experience with bioinformatics pipelines computational biology biostatistics medical imaging analysis or clinical data analysis.
Strong understanding of experimental design statistical inference and reproducibility in biomedical research.
Proven experience reviewing code analyses manuscripts or research outputs.
Experience mentoring or leading small technical or research teams.
Excellent written communication skills for providing precise constructive feedback.

Preferred Qualifications:

Familiarity with tools such as samtools bcftools GATK BWA STAR Salmon DESeq2 edgeR Seurat Scanpy Biopython Bioconductor Nextflow or Snakemake.
Familiarity with RDKit OpenMM GROMACS AutoDock Vina PyMOL or Biopython structure tools.
Familiarity with medical imaging and clinical tools such as pydicom nibabel SimpleITK MONAI FSL lifelines statsmodels or FHIR data formats.
Familiarity with Docker Conda Git CI systems automated testing and HPC environments.
Experience designing benchmarks analysis challenges or computational evaluations.

Bonus Points:

Experience evaluating AI coding or terminal agents.
Experience with data governance IRB processes or biosafety review.
Industry experience in biotech pharma clinical research or health technology.
Publications or open-source contributions in computational biology bioinformatics or biomedical informatics.
Prior experience in AI training or evaluation programs.

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