Senior Machine Learning Engineer (AI Agent)
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What you'll do
- 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
What they're looking for
- 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.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
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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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
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.
Company
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