Software Development Engineer in Test (SDET)

Role Overview

Software Development Engineer in Test (SDET) for a voice AI customer service platform. You’ll help build a proactive, automated, AI-native quality engineering practice by partnering with Engineering, Product, and Delivery to strengthen validation across the system.

This is a hands-on engineering role focused on test strategy, automation frameworks/tooling, and turning real customer/product insights into stronger testing and quality gates.

What You’ll Do

• Serve as a senior technical authority for quality engineering, shaping how quality is built into the platform.
• Raise the technical bar across a team of AI-enabled QA Engineers through code/test review, pairing, mentorship, and hands-on examples.
• Drive an AI-native quality engineering strategy to:
  • scale coverage
  • accelerate validation
  • surface failures earlier
• Build AI-driven testing workflows for broad, scalable validation across the platform.
• Own the end-to-end quality feedback loop, including:
  • internal dogfooding
  • customer A/B testing
  • customer feedback triage and bug reporting
  • translating real-world usage into better coverage and product decisions
• Partner early with Product/Engineering to improve testability, define acceptance criteria, and build quality into delivery.
• Build and improve automation frameworks, validation tooling, and CI/CD quality gates for reliable, repeatable releases.
• Use AI and analytics to expand edge-case coverage, summarize failures, and generate insights for roadmap/release decisions.

What You’ll Bring

• 5+ years in SDET or quality engineering, with significant hands-on coding and experience owning test strategy and release quality in complex systems.
• Strong programming skills in TypeScript, JavaScript, or Python, able to build and own production-quality tooling/frameworks.
• An AI-native approach to quality engineering (leveraging AI to increase coverage, reduce manual effort, and improve workflows).
• Curiosity about AI systems and conversational/voice experiences, including the nuances of testing non-traditional behavior.
• Experience influencing and collaborating across teams to establish a shared quality bar.
• Deep knowledge of modern testing practices:
  • automation
  • API and integration testing
  • regression strategy
  • CI/CD-based quality workflows
• Mastery of test design and risk-based coverage (what to automate vs. what to explore manually).
• Comfort using data, metrics, and analytics to evaluate quality and communicate trends/risks/opportunities.
• Excellent collaboration/communication across Engineering, Product, and customer-facing teams.

Nice to Have

• Experience testing AI and LLM-powered systems, including evaluating non-deterministic behavior.