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Member of Technical Staff – Proteomics Research

insilico search partners • San Francisco Bay Area
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Lead the company's proteomics benchmark and define evaluation standards for how AI agents interpret proteomic data. Design technical approaches and lead a team building benchmark datasets across proteomics, metabolomics, and lipidomics workflows. Requires 5+ years hands-on LC/MS proteomics experience, coding proficiency with AI agents, and prior contribution to a novel proteomics method or published pipeline.

Key Highlights
Lead a team building benchmark datasets across proteomics, metabolomics, and lipidomics workflows from raw instrument QC through differential analysis
Define evaluation standards for measuring AI agent performance on proteomic data interpretation
Map end-to-end MS-based workflows into benchmark-ready tasks including multi-omic integration and clinical interpretation
Key Responsibilities
Lead a team building benchmark datasets across proteomics, metabolomics, and lipidomics workflows from raw instrument QC through differential analysis
Define evaluation standards for how AI agents are measured on proteomic data interpretation
Map end-to-end MS-based workflows into benchmark-ready tasks including multi-omic integration and clinical/translational interpretation
Technical Skills Required
Proteomics LC/MS AI Agents
Benefits & Perks
Profit share and equity
401(k) contribution
100% covered health, dental, and vision
Unlimited PTO
Work visa sponsorship
Daily lunch and dinner
Relocation assistance
Company-sponsored professional development
Nice to Have
Experience with metabolomics or lipidomics from a core facility, platform company, or pharma biomarker group

Job Description

Member of Technical Staff – Proteomics

Research · Full-Time San Francisco, CA (on-site)

Our client builds rigorous scientific benchmarks that measure how well AI agents perform real biology, used by frontier AI labs and pharma companies to test whether agents can handle the messy, multi-layer reasoning real biological work demands. Every benchmark is grounded in real experimental and clinical work, and built by scientists who've done that work themselves.

Opportunity

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