Why This Job is Featured on The SaaS Jobs
This Sr. SDET role sits at the intersection of SaaS reliability and the newer class of AI-native product surfaces, where voice agents, orchestration layers, and LLM behaviors become part of the core user experience. In the SaaS ecosystem, quality engineering for these systems is increasingly differentiated because it must account for both conventional distributed services and non-deterministic model outputs. The scope across UI, APIs, and audio or text interactions reflects how modern SaaS products are delivered as end-to-end workflows rather than isolated features.
From a long-term SaaS career perspective, the work builds durable leverage in automation strategy, CI-integrated validation, and observability practices that translate across cloud products. Exposure to evaluation frameworks, probabilistic testing, and quality KPIs maps to how SaaS teams are learning to operationalize AI features with measurable standards. Owning frameworks rather than only test cases also signals experience that scales with product complexity.
This position best fits an engineer who prefers building tooling and systems that other teams depend on, and who is comfortable partnering closely with developers and applied AI specialists. It aligns with a senior profile that enjoys defining quality approaches amid evolving requirements, particularly in cloud-native environments where release confidence is tied to automation depth and instrumentation.
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.
