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. Minimum Qualifications 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). Preferred Qualifications 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.