PatSnap
501 - 1000 employees
Founded 2007
🤖 Artificial Intelligence
☁️ SaaS
🏢 Enterprise
💰 $300M Series E on 2021-03
Artificial Intelligence • SaaS • Enterprise
PatSnap is a leading provider of IP and R&D innovation intelligence platforms. It leverages advanced AI technologies to help companies enhance productivity, drive innovation, and accelerate business growth by providing insights into patent and R&D processes. PatSnap's offerings aim to reduce R&D costs, assist in the discovery of new materials, and support life sciences intelligence. It provides a comprehensive database and analysis tools that are used by innovators to automate sequences, assess risk, and enhance innovation strategies.
Materials Science Intern
🔥 2 minutes ago
🗣️🇨🇳 Chinese Required
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PatSnap
501 - 1000 employees
Founded 2007
🤖 Artificial Intelligence
☁️ SaaS
🏢 Enterprise
💰 $300M Series E on 2021-03
Artificial Intelligence • SaaS • Enterprise
PatSnap is a leading provider of IP and R&D innovation intelligence platforms. It leverages advanced AI technologies to help companies enhance productivity, drive innovation, and accelerate business growth by providing insights into patent and R&D processes. PatSnap's offerings aim to reduce R&D costs, assist in the discovery of new materials, and support life sciences intelligence. It provides a comprehensive database and analysis tools that are used by innovators to automate sequences, assess risk, and enhance innovation strategies.
📋 Description
• Label entities such as material identities, composition specifications, material states, and sources. • Record composition constituents, amounts, and units exactly as written. • Capture stated links such as material-to-composition, material-to-state, claim/example relationships, and coreference. • Preserve span IDs and verbatim text needed for later resolution. • Leave external classification, normalization, equivalence mapping, unit defaulting, and material-family decisions to a separate expert resolution process. • Nice to have/stretch: help develop or evaluate LLM-assisted annotation workflows that improve productivity while preserving human review quality.
🎯 Requirements
• Strong reading comprehension for technical English and/or Chinese. • Familiarity with materials science, metallurgy, chemistry, patents, or technical documentation. • Ability to preserve exact spans, wording, units, ranges, and symbols. • Attention to document context, especially references such as "said alloy," "Example 2," or "the composition." • Comfort following structured annotation guidelines and recording labels consistently. • Good judgment about when information is explicitly stated versus inferred from outside knowledge.
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