Senior Machine Learning Engineer (AI Agent)
We're hiring a Senior NLP Engineer to own the accuracy of our information-extraction pipeline. You'll work on hard problems extracting material properties and quantitative measurements from long, complex documents — patents and scientific literature where the signal is scattered across sections and relationships span paragraphs. This is a hands-on senior role with end-to-end ownership: you'll independently scope, prototype, and ship algorithmic improvements, define how we measure quality, and raise the extraction precision bar to production grade.
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Check out this short overview:
This is an in-office position based in our Singapore office.
Who are we?
Patsnap is a global, pre-IPO company that transforms the way organizations harness their Intellectual Property and Research & Development productivity. Our platform revolutionizes how IP and R&D teams collaborate across the entire innovation lifecycle, using domain-specific AI to accelerate the creation of market-ready products. With over 12,000 customers worldwide, including some of the biggest names in innovation, Patsnap is at the forefront of technological advancement. Our $300M Series E funding round brings our valuation to a $1 billion unicorn status, and we still have a remarkable amount of growth ahead.
We have a vibrant and diverse team with offices in Singapore, Toronto, London, Shanghai and remote teams based in US. Our hyper-growth trajectory is powered by our people, and we are extremely proud of our company-wide vision, work ethic, and entrepreneurial spirit. We are committed to fostering an inclusive environment where talent thrives and ideas bloom.
What You'll Be Doing:
Responsible for analysing and understanding of the massive structured and unstructured data by NLP tasks, such as data processing, NER, relationship extraction, NOR etc.
Own and improve our information-extraction pipeline, from model to production
Solve document-level extraction at scale — design how we handle long documents where entities, properties, and their relationships span across sections
Independently drive algorithm optimization, develop and fine-tune models to meet business requirements.
Build hybrid systems combining NER, rule-based methods, and LLMs, applying each where it genuinely wins
Define evaluation methodology / annotation strategy / quality metrics with domain experts
Process and extract from large-scale corpora reliably and efficiently
Requirements
Master or Bachelor degree in Computer Science or related field.
4+ years of hands-on NLP/ML engineering in production, owning model or pipeline quality
Proven experience with long-document / document-level information extraction and normalization
Solid engineering fundamentals from working at scale
Strong in NER, entity methodology; across rule-based, statistical, and LLM-based methods
Experience in vibe coding and solid coding skills.