Software Engineer (Quality), Retail and Marcom Engineering

As a Software Engineer, you’ll prototype quickly, tie engineering to business outcomes, and balance rigor with AI-driven velocity. You’ll build and extend the quality platform—orchestration, retrieval, evaluation harnesses, and APIs—that scales quality across the portfolio. Build platform services that normalize data from PRs, specs, logs, and test runs and surface signals in a central dashboard Evolve the execution layer by operating shared runners with fair queues and hardened images and by adding observability and repeatable test data to reduce infra flakiness and speed time to signal Codify guardrails by implementing thresholds and Pass or Warn or Block verdicts with lightweight change history Prototype quickly and deliver end to end from design to launch with fast, disciplined iterations Partner across Engineering to drive adoption and a simple developer experience Ship agentic workflows to automate test generation, defect triage, and CI/CD gates Integrate LLM and retrieval to power internal QE copilots and PRs or Slack embeds Requires Bachelor’s degree in Computer Science, a technical field or a minimum of 5 years of relevant work experience. Proficiency in Node or Python and ability to read/write the other. Experience with RESTful/GraphQL APIs and automated testing frameworks (e.g., Playwright, Jest, Selenium, XCUITest). Experience either improving reliability of shared CI/test infrastructure (e.g., lower infra flakiness, faster queue/start times) or operating a CI runner fleet (agents/executors; GitHub Actions, Jenkins, or Harness). Shipped one LLM-powered feature (e.g., RAG over internal docs/telemetry; triage agent; stability investigator; natural-language to automation; eval harnesses; CI gates for accuracy/latency). 8+ years as a Software Engineer and 5+ years in automation or platform tooling. Retrieval + RAG expertise (e.g., Pinecone, Qdrant, OpenSearch; re-ranking, hallucination evals, chunking strategies). Strong Python for ML/LLM workflows; advanced TypeScript and Node for platform services. Platform integration of AI agents, quality policies, and data-driven dashboards. Strong fundamentals in mocking, dependency injection, and distributed systems. Experience building shared execution images/runners and multi-tenancy controls (hardened, versioned images; isolation/quotas; fair queuing) on a company internal cloud (e.g., Kubernetes or an internal scheduler). Experience with cross-platform automation frameworks across web, native, and APIs; and reducing flakiness and improving time-to-signal via heuristics/stats. Experience building and integrating REST and federated GraphQL (e.g., Apollo Federation v2) services, including subgraph/endpoint development, schema/API composition, and deployment collaboration with platform teams. Experience creating deterministic test data: seeding known records, masked subsets, synthetic data; managing golden dataset versioning and lineage. Ability to explain complex systems simply to engineers and non-engineers.

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