Software Quality Engineer (us)

Writer Writer · AI Frontier · San Francisco, CA · Engineering, product & design

Software Quality Engineer responsible for ensuring the quality, reliability, and performance of AI-powered work orchestration platform, including AI agents and LLM applications. This involves defining QA strategies, developing automation frameworks, collaborating with ML engineers and data scientists, and monitoring production systems.

What you'd actually do

  1. Define and implement comprehensive quality assurance strategies and test plans for our AI agents and LLM-powered applications, ensuring exceptional product reliability and performance.
  2. Designing and developing automation frameworks: creating robust, scalable, and maintainable automated test frameworks from scratch or enhancing existing ones. You’ll need proficiency in at least one language like Typsecript, Python.
  3. Collaborate closely with product managers, machine learning engineers, and data scientists to understand complex AI features and model behaviors, translating them into effective test cases and validation criteria.
  4. Drive the continuous improvement of our testing processes and infrastructure, integrating automated checks within our CI/CD pipelines to ensure rapid, high-quality releases.
  5. Monitor production systems and AI model performance, proactively identifying potential issues and contributing to post-release quality validation.

Skills

Required

  • 5+ years of hands-on experience in software quality assurance or engineering
  • testing complex distributed systems or AI/ML applications
  • Proficiency in programming languages like Python or Typescript for test automation
  • experience with modern testing frameworks such as Playwright
  • Solid understanding of AI/ML concepts, including model evaluation metrics, data pipelines, and the unique challenges of testing generative AI outputs
  • Exceptional analytical skills
  • keen eye for detail
  • Collaborative spirit
  • Demonstrated ability to drive initiatives independently

Nice to have

  • cloud platforms (AWS, Azure, GCP)
  • containerization technologies (Docker, Kubernetes)
  • CI/CD environment
  • Experience designing, developing, or integrating agentic AI systems, AI skills, and the Model Context Protocol (MCP)

What the JD emphasized

  • rigorous quality strategies
  • enterprise-grade LLMs
  • AI agents
  • AI quality assurance
  • testing complex distributed systems or AI/ML applications
  • testing generative AI outputs
  • AI skills

Other signals

  • AI agents
  • LLM-powered applications
  • enterprise-grade LLMs