Pod lead - Chemical Engineering (Terminal Bench Science)
Core
Lead a pod of technical trainers to develop realistic, terminal-based scientific and engineering tasks for frontier AI training and evaluation in life sciences.
Role type
Senior IC Pod Lead (Computational Life Sciences)
Builds
Realistic agentic scientific workflows, datasets, instructions, constraints, and verifiable ground truths for AI agents.
Domain
Life Sciences / Computational Biology / AI Training
Deliverable
production ML models
Required skills
Scientific programming (Python), Bioinformatics, Computational Genomics, Systems Biology, Biostatistics, Computational Drug Discovery, Structural Biology, Protein Engineering, Computational Microbiology, Team Leadership, Quality Assurance, Task Design, Data Validation, Biosafety Review
Preferred skills
Tools (samtools, bcftools, GATK, BWA, STAR, Salmon, DESeq2, edgeR, Seurat, Scanpy, Biopython, Bioconductor, Nextflow, Snakemake, RDKit, OpenMM, GROMACS, AutoDock Vina, PyMOL, pydicom, nibabel, SimpleITK, MONAI, FSL, lifelines, statsmodels, FHIR), Docker, Conda, Git, CI systems, HPC, Benchmark Design, AI Agent Evaluation, Data Governance, IRB processes
Technologies
Python, samtools, bcftools, GATK, BWA, STAR, Salmon, DESeq2, edgeR, Seurat, Scanpy, Biopython, Bioconductor, Nextflow, Snakemake, RDKit, OpenMM, GROMACS, AutoDock Vina, PyMOL, pydicom, nibabel, SimpleITK, MONAI, FSL, lifelines, statsmodels, FHIR, Docker, Conda, Git, CI systems, HPC
Responsibilities
Review and validate trainer tasks for scientific accuracy, reproducibility, and quality; Mentor trainers and manage pod performance; Design challenging agentic workflows and create reproducible expert solutions; Validate scientific assumptions, code, and outputs; Screen tasks for biosafety and dual-use concerns; Ensure data compliance and synthetic/public licensing; Provide feedback and track revisions.
Seniority
Senior, hands-on IC
