Algorithm Test Development Engineer

2 weeks ago


Singapore PatSnap Full time
Company Overview
PatSnap is a SaaS-based tech innovation intelligence service provider, dedicated to helping R&D leaders maximize the value of innovation intelligence within their R&D workflow and strategic planning.

Department Overview
The R&D SG Department focuses on building leading capabilities in natural language processing technology and services, leveraging technologies such as Deep Learning, NLP, CV, and Knowledge Graph to create top-tier products.

Job Responsibilities
  • Responsible for the design and implementation of AI-related business needs evaluation;
  • Responsible for the corpus construction and test capability assessment of AI-related engine capabilities;
  • Responsible for the construction of the algorithm test environment and platform;
  • Algorithm evaluation, including requirement analysis, test plan, corpus construction, design indicators based on production needs and coding, defect tracking and quality analysis, etc.
  • Responsible for establishing a system for subjective and objective evaluation of algorithm quality;
  • Actively cooperate with the data team and R&D team to optimize the process and methods of testing, data collection, and annotation, and to improve the quality and efficiency of the team's work;

Requirements
  • Proficient in at least one of Python, C++, Java, and other programming languages, proficient in database languages such as SQL, etc., familiar with the Linux operating system, and have a solid programming foundation;
  • Familiar with mainstream automated testing open-source frameworks and tools, and can maintain and optimize these frameworks or tools;
  • Have experience in AI product/algorithm testing, proficient in algorithm quality evaluation;
  • Familiar with the principles of deep learning and common machine learning algorithms, and can use clustering, classification, regression, ranking and other models to solve problems;
  • In-depth understanding and application of common language model evaluation indicators such as Cross-entropy, BPC/BPW, Perplexity, ROUGE, etc.
  • Strong insight and practical ability to learn new technologies;
  • Familiar with the software development lifecycle.


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