Why This Job is Featured on The SaaS Jobs
Why this Role is Featured on The SaaS Jobs
Quality engineering is being redefined in SaaS as AI moves from add-on features to core product workflows. This Sr. SDET role sits directly in that shift, focusing on agentic QA for an AI Voice Agent service where outcomes are partly non-deterministic and user experience spans UI, APIs, and audio or text interactions. The remit signals a modern SaaS surface area: orchestration flows, configuration experiences, and safety guardrails that must hold up under real customer usage.
For a SaaS career, the standout value is building evaluation and automation infrastructure that treats quality as a measurable system, not a final gate. Experience here translates into patterns increasingly common across SaaS teams adopting LLMs: probabilistic validation, CI-integrated quality checks, observability for debugging, and performance testing that balances latency and reliability. Ownership of frameworks and KPIs also strengthens the ability to influence product readiness across the release cycle.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Your role
As a Sr. SDET in Agentic QA, you will own the test automation and quality frameworks that support Dialpad’s AI Voice Agent services. You will develop automated tests for end-to-end product experiences, from frontend UI to backend services to APIs to audio/text interactions. You will test orchestration flows, agent configuration experiences, and guardian safeguards to create robust automated coverage for functionality, performance, reliability, UX, and more.
In this role, you will develop substantial amounts of automated test infrastructure and partner deeply with the development team to make our fast-growing AI platform more testable, more stable, and more delightful for customers.
This position is based at one of Dialpad’s Canadian offices and reports to a QA Eng Manager in the United States.
What you’ll do
- Own end-to-end quality for agentic features and workflows, including strategy, development, execution, and release qualification.
- Design and build automation tooling and frameworks for AI/LLM-driven systems, including prompt flows, agent orchestration, and tool integrations.
- Develop and maintain evaluation frameworks (evals) to measure response quality, accuracy, and hallucination rates.
- Drive automation coverage (80%+ for critical AI workflows) using deterministic + probabilistic validation approaches.
- Integrate AI quality checks into CI/CD pipelines with fast feedback cycles (<15 minutes for PR validation).
- Build tooling for LLM observability and debugging, including prompt tracing and response analysis.
- Partner with Applied AI teams on prompt engineering, model selection, and evaluation strategies.
- Design and execute performance and load tests for AI services (latency, throughput, cost efficiency).
- Identify and mitigate risks related to hallucinations, bias, safety, and edge cases.
- Define and track AI quality KPIs (task success rates, precision/recall, latency, etc.).
- Participate in design and architecture reviews to ensure systems are testable, observable, and resilient.
- Mentor engineers and contribute to raising the bar on AI quality engineering practices.
What you’ll bring
- 5+ years of experience in software engineering or SDET roles with an emphasis on software development.
- Strong programming skills in Python (preferred), Java, or JavaScript.
- Experience testing distributed, cloud-native SaaS systems and APIs.
- Demonstrated proficiency in coding with AI agents to accelerate development and improve code quality.
- Hands-on exposure to LLMs or AI/ML systems (e.g., OpenAI, Claude, Gemini, or similar platforms).
- Understanding of non-deterministic systems and probabilistic testing approaches.
- Experience building test frameworks and scalable automation systems.
- Familiarity with AI evaluation techniques (benchmarking, golden datasets, human-in-the-loop validation).
- Experience with CI/CD pipelines (e.g., Jenkins, GitHub Actions).
- Strong collaboration skills with the ability to work across distributed teams and time zones.
- Bachelor’s degree in Computer Science or equivalent practical experience.
- Backend: Python, Go, Google Cloud Platform, Cloud Run / App Engine, Kubernetes, Datastore, Redis, ElasticSearch.
- Frontend: Vue3, React.
- AI Stack: LLM APIs, LiveKit, prompt orchestration frameworks, evaluation tooling.
