wagey.ggwagey.ggv1.0-68eec7a-3-May
Browse Tech JobsCompaniesFeaturesPricingFAQs
Log InGet Started Free
Jobs/Machine Learning Engineer Role/Patsnap - Senior Machine Learning Engineer / Machine Learning Expert
Patsnap

Patsnap - Senior Machine Learning Engineer / Machine Learning Expert

Singapore - Hybrid3mo ago
In OfficeSeniorAPACArtificial IntelligenceCloud ComputingMaterialsHigher EducationMachine Learning EngineerEntry LevelClaudePythonDocumentationVectorAWS

Upload My Resume

Drop here or click to browse · Tap to choose · PDF, DOCX, DOC, RTF, TXT

Apply in One Click
Apply in One Click

Requirements

• Bachelor's degree or higher in Computer Science, Artificial Intelligence, or a related quantitative field. • Proficient in leveraging AI-enhanced development tools such as Claude Code, solid programming skills in Python with a focus on algorithmic implementation. • 3+ years of hands-on experience in NLP/LLM/RAG, with a proven track record of deploying complex information extraction systems in production. • Excellent communication skills with the ability to understand product requirement, solve data/RAG related challenges within a cross-functional team. • RAG & Data: Deep experience in high-quality data processing and building production-grade RAG systems (including vector databases, hybrid search, and re-ranking). • Model Refinement: Proven ability to enhance extraction accuracy through post-training techniques, including NER, prompt engineering, and instruction fine-tuning. • Domain Expertise: Familiarity with Physics, Chemistry, or Biology (e.g., understanding of molecular structures or material properties) is a significant plus.

Responsibilities

• Data Extraction Pipelines: Develop end-to-end solutions integrating OCR, Layout Analysis, and Semantic Parsing to precisely capture chemical formulas, experimental parameters, and performance metrics from complex documents. Resolve cross-modal data alignment between body text and complex scientific visuals (e.g., tables, chemical structures, and charts). • RAG Development: Partner with Data and Agent teams to design and implement robust RAG (hybrid search, ranking optimization) and search architectures, ensuring highly relevant document retrieval for complex scientific queries. • Model Post-training: Utilize post-training skills to enhance LLMs' reasoning capabilities and understanding on Materials Science data extraction.

Get Started Free

No credit card. Takes 10 seconds.

Privacy·Terms··Contact·FAQ·Wagey on X