Senior Machine Learning Engineer (AI Agent)

Patsnap; Singapore; On-site; SGD 150,000 - 190,000; Full time

Job summary

Patsnap is seeking a Senior Machine Learning Engineer to own an information-extraction pipeline for patents and scientific literature. You will prototype, ship improvements, and raise extraction precision to production-grade level.

You will work hands-on on long documents, integrating NER, rules, and LLMs, with end-to-end ownership from modeling to deployment in an in-office Singapore role.

Qualifications

• Bachelor's or Master's in CS or related field.
• 4+ years of hands-on NLP/ML engineering in production.
• Proven experience with long-document information extraction and normalization.
• Solid engineering fundamentals for working at scale.
• Strong in NER and entity methods across rule-based, statistical, and LLM-based approaches.
• Strong coding skills.

Responsibilities

• Analyse and understand large structured and unstructured data using NLP tasks (data processing, NER, relationship extraction, NOR).
• Own and improve the information-extraction pipeline from model to production.
• Address document-level extraction challenges across long documents and cross-section relationships.
• Drive algorithm optimization and fine-tune models to meet business needs.
• Build hybrid systems combining NER, rules, and LLMs where each excels.
• Define evaluation, annotation strategy, and quality metrics with domain experts.
• Process and extract from large-scale corpora reliably and efficiently.

Skills

NLP/ML engineering

Document-level extraction

NER

LLMs & hybrid systems

Model optimization

Production readiness

Education

Master's or Bachelor's degree in Computer Science or related field

Job description

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

Job 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.