Open role at a frontier AI lab
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In this role, you’ll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.
Key Responsibilities:
• Design, evaluate, and critique bioinformatics and computational biology workflows to support AI benchmarking initiatives.
• Provide expert-level insights on genomic, transcriptomic, and proteomic data analysis pipelines.
• Assess and optimize pipeline parameters, tool configurations, and statistical methods for accuracy and reproducibility.
• Analyze biological datasets (e.g., NGS, RNA-seq, variant calling, protein structure) and provide actionable recommendations.
• Leverage domain knowledge to guide the development and validation of biology-related AI models and tools.
• Communicate complex scientific concepts clearly, both in writing and verbally, to technical and non-technical collaborators.
• Document findings, methodologies, and best practices to ensure knowledge transfer across the customer’s team.
Required Skills and Qualifications:
• PhD in bioinformatics, computational biology, genomics, or a closely related field, or equivalent industry/research experience.
• Professional written and spoken English (C1 or higher).
• Hands-on experience analyzing biological data (e.g., NGS, RNA-seq, variant analysis).
• Proficiency in Python and/or R for scientific analysis, plus experience with standard bioinformatics tools (e.g., BLAST, samtools, GATK, Bioconductor, Biopython).
• Experience building reproducible workflows with tools such as Nextflow or Snakemake, and comfort working in Linux/command-line environments.
• Strong statistical, quantitative, and analytical reasoning skills.
• Outstanding written and verbal communication abilities, with the ability to clearly document and explain technical concepts.
• Track record of rigorous scientific problem-solving in complex biological domains.
• Experience with AI/ML-assisted biology workflows or benchmarking.
• Peer-reviewed publications, patents, or contributions to open-source bioinformatics software.
• Background in single-cell analysis, structural biology, proteomics, or drug discovery.
• prediction/retrosynthesis.