Checking in- (100% remote considered) Are you interested in a Sr. Software Development Engineer in Test (SDET) -( NLP required)Testing opportunity to work with a company focused on Healthcare Systems solutions

  • Posted 41 days ago | Updated 10 days ago


Depends on Experience
Full Time



Job Details

Sr. Software Development Engineer in Test (SDET) - NLP Testing
Full Time opportunity-Office located in San Francisco, CA (100% remote considered)

We are seeking a talented and experienced Senior Software Development Engineer in Test (SDET) with a strong background in Natural Language Processing (NLP) testing and expertise in backend services on AWS or Google Cloud Platform.


Develop, maintain, document, and execute test plans and automated test scripts

Collaborate with developers, Product Managers to understand business requirements to develop manual and automated test strategies that will test ASR and NLP functionality

Implement test strategies to validate the accuracy and performance of NLP algorithms

Build and execute functional, integration, performance, scalability tests to ensure our NLP pipelines are working as expected
Design and create test data (text, speech, etc) as needed to verify diverse user scenariosI dentify, document, and track defects thoroughly,including detailed steps to reproduce

Maintain detailed documentation of test plans, test cases, and testing results

Continue to develop NLP, testing, and automation skills through ongoing professional development training, bringing new ideas and solutions to the team


Bachelor's degree in Computer Science, Software Engineering, or a related field

Proven experience as an SDET with a focus on NLP testing and cloud services

Strong programming skills in Python

Experience with automated testing tools and framework knowledge of cloud services. Familiarity with Agile/Scrum methodologies

Familiarity with API testing principles (Python or Postman). Strong understanding of the Software Development Life Cycle (SDLC)

Bonus Qualifications:
Knowledge and some experience in using LLMs to identify NLP mistakes