Senior QA Engineer - AI/LLM at Binance
Remote, , Singapore -
Full Time


Start Date

Immediate

Expiry Date

09 Dec, 25

Salary

0.0

Posted On

09 Sep, 25

Experience

3 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Test Planning, Ownership, Computer Engineering, Computer Science

Industry

Information Technology/IT

Description

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by over 280 million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, Web3 features, and more. We leverage the power of digital assets and blockchain to build an inclusive financial ecosystem to advance the freedom of money and improve financial access for people around the world.

REQUIREMENTS:

  • Bachelor or Master in computer science, computer engineering or related area.
  • At least 3+ years of software QA engineering experiences.
  • Full cycle testing with test planning and both manual/automation execution.
  • Solid Java coding skills, and experienced in automation or QA tools development.
  • Good understanding in AI applications and LLM prompt engineering techniques. Experiences in LLM development or eval is a plus.
  • Excellent self-driven and ownership with good deliverables.
  • Eager to learn, be curious about AI and new technologies of LLM.
  • Good communication and collaboration skills.
Responsibilities
  • Participate in the entire software development lifecycle, encompassing all stages from requirements analysis to test planning, execution, defect tracking, through to product release and maintenance.
  • Create comprehensive and effective test strategies and hands-on testing to ensure the accuracy, reliability, and performance of AI and data applications .
  • Root cause analysis of test failures and product issues in an effective manner, and drive optimization for future enhancements.
  • Design and develop internal tools leveraging AI technology to improve engineering and testing work efficiency.
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