Remote Pod Lead - Life Science

Turing

1d•Remote

About The Position

Turing is seeking an experienced life sciences or medicine expert to lead a pod of trainers developing realistic, terminal-based scientific and technical tasks for Terminal Bench Science. The Pod Lead reviews every task for scientific correctness, reproducibility, data compliance, and grader reliability, and coaches trainers to produce consistently high-quality, challenging tasks. The role includes end-to-end review of task statements, datasets, reference files, pipelines, environments, reference solutions, and automated tests. The Pod Lead verifies that tasks reflect authentic research or clinical analysis workflows and require genuine multi-step scientific reasoning. They check that tools, reference genomes, annotation versions, databases, and dependencies are pinned and pipelines are reproducible; validate scientific assumptions and results; and ensure graders assess substantive outputs while accommodating legitimate stochastic and numerical variation. The Pod Lead also checks that tasks cannot be gamed, confirms data is synthetic or public, appropriately licensed, and de-identified, and screens for biosafety and dual-use concerns. They provide actionable written feedback, track revisions through closure, onboard and mentor trainers, allocate work, monitor quality and throughput, escalate blockers, and maintain pod documentation. This is a contractor assignment for five weeks, with an expected start next week. The commitment is at least six hours per day and 40 hours per week, including a four-hour overlap with PST. The assignment does not include medical or paid leave.

Requirements

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, and proficiency with Linux and terminal environments.
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.

Nice To Haves

Familiarity with 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.
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.

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 scientific outputs rather than formatting, and that tolerances allow 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 data is synthetic or public, appropriately licensed, and de-identified, with no protected health information or restricted-access data.
Screen tasks for biosafety and dual-use concerns, and 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.