Software Development Engineer in Test (SDET)

Role Summary

We are looking for a hands-on Software Development Engineer in Test (SDET) to join our AI-native product development team at onsemi and own end-to-end quality and release certification for a full-stack platform built on Next.js, React, TypeScript, Django, PostgreSQL, Redis, GraphQL, Airflow, MCP-based AI agents, Kubernetes, Helm, and Docker. This is a full-stack testing role covering UI, API, data, async job, and AI-agent layers, with accountability for certifying that each release meets functional, non-functional, and reliability bars before reaching production. The candidate should build durable automation frameworks and make heavy, responsible use of AI coding agents to design test strategy, generate and maintain suites, and triage failures faster.

Success Expectations

You should independently own end-to-end quality for the platform: building and maintaining a full-stack automated test pyramid (unit, API, integration, E2E, non-functional, and AI-agent evaluation), certifying releases against clear, evidence-based exit criteria, using AI coding agents responsibly to accelerate test authoring and triage, and partnering with engineering to continuously reduce defect escape rate without slowing delivery.

• 3 to 5 years in software quality engineering, test automation, or SDET roles, with demonstrable ownership of release certification for a production system.
• Strong hands-on experience with Playwright (or equivalent) for UI-driven end-to-end testing of React/Next.js apps, including async state and long-running workflows.
• Strong hands-on experience with API/backend test automation (pytest, pytest-django, or equivalent), including REST and GraphQL testing.
• Working knowledge of component/unit testing with Vitest, Jest, or React Testing Library.
• Solid understanding of relational databases, test data management, and seeding/cleanup for repeatable automated runs.
• Experience testing asynchronous, job-queue-based, or event-driven systems (Redis, background workers, Airflow or similar orchestration pipelines).
• Experience designing non-functional test strategies: performance/load, resilience/chaos testing, and baseline security testing.
• Practical experience with Docker, Kubernetes, and Helm, and CI/CD pipelines as quality gates.
• Strong, demonstrable experience using AI coding agents (GitHub Copilot, Claude Code, Cursor, or similar) to generate and maintain test automation, with the judgment to validate AI-generated tests.
• Understanding of AI-agent/LLM application testing: prompt/response evaluation, tool-call verification, and agentic regression/guardrail testing.

• Excellent defect triage and cross-functional communication skills; ability to make an independent, defensible release go/no-go call.

• Own test strategy and automation across the stack: unit/component tests (Vitest, React Testing Library), API/integration tests (pytest, pytest-django, GraphQL), and UI-driven end-to-end tests (Playwright) against real workflows.
• Own release certification: define exit criteria, run and triage full regression suites (including sequential, long-running workflow integration tests), and give a clear go/no-go signal per release.
• Maintain functional coverage for core workflows: project/variant management, GDS and model linking, test bench execution, regression dispatch and reporting, compliance/RTM tracking, sharing/permissions, and notifications.
• Build test coverage for the AI Agent Workspace: prompt/response evaluation harnesses, tool-call correctness, contract tests for MCP-based agent tool servers, RAG pipeline retrieval/grounding accuracy, agent memory and session-persistence correctness, agent regression/eval suites, and guardrails against unsafe agent actions.
• Design and execute non-functional testing: API/GraphQL performance and load testing, resilience/failure-injection testing for async job pipelines, and baseline security testing (authN/authZ, RBAC, input validation).
• Validate the decoupled async execution model end-to-end - Airflow-orchestrated job dispatch, the containerized GDS/layout processing engine, and the Windows-hosted execution runner that drives external EDA tool runs - through result ingestion, including monitoring for stuck or failed jobs.
• Own visual and functional parity testing between the design prototype and the production frontend.
• Integrate and maintain automated quality gates in CI/CD, including flake detection, test data seeding/cleanup, and enviro…