Company Description
LVIS Corporation is on a mission to revolutionize the entire continuum of neurological care by developing AI driven software tools to help physicians accelerate patient throughput and improve patient diagnostic and treatment outcomes. We have an international team with our headquarters located in Palo Alto, California, USA, and we have an office in Gangnam, Seoul, South Korea. We are looking for talented individuals to join us in our mission to transform the neurology health care industry.

Requirements

• Bachelor’s or Master’s degree in Computer Engineering or Electrical Engineering.
• Over 10 years of experience in software testing.
• In-depth knowledge and hands-on experience with software QA methodologies, tools, and processes.
• Proficient in both manual and automated testing techniques.
• Skilled in programming languages like Java or Python and familiar with CI/CD tools.
• Solid understanding of SQL and Linux environments.
• Professional expertise in tools such as Jenkins, Jira, and Git.
• Proven experience in developing and maintaining automation frameworks and test cases for both frontend and APIs.
• Experience in load and performance testing is an advantage.
• Exceptional analytical and troubleshooting skills, with a self-motivated attitude and eagerness to quickly learn new concepts, technologies, and products.
• Excellent written and verbal communication skills.
• Capable of working independently.

What you will be doing
As a Senior Software QA Engineer, you will play a pivotal role in the LVIS QA team, ensuring exceptional quality across our product suite. Collaborating closely with the Cloud and Platform Engineering and Product teams, your responsibilities will include:

• Developing and executing test plans, as well as writing and running test cases for the LVIS Neuro Match® platform.
• Maintaining and/or creating automation tools and frameworks to streamline functional and regression testing.
• Designing and implementing various types of software tests, such as unit, functional, performance, or stress testing.
• Creating and automating test cases that interact with both SQL and No SQL databases.
• Applying knowledge of microservices architecture, including service orchestration and inter-service communication (e.g., REST, g RPC, or messaging systems like Kafka or Rabbit MQ).
• Leveraging an understanding of machine learning algorithms and statistical principles to validate and QA data and models effectively.