Forward Deployed Engineer, Maps Client QE Intelligence

The Maps Client Quality Engineering Intelligence (QEI) team builds AI-native tooling used every day by the Maps Client Quality Engineering organization QE leads SDETs and engineering managers. Our work lands directly in their triage sessions release readiness reviews test coverage automation root cause analysis and a Forward Deployed Engineer on QEI you will stay in tight communication with the engineers and leads we serve learning their workflows surfacing where they get stuck and identifying which tools would change their day then translating what you learn into the tools you build and ship. Adoption is what we optimize for measured over a release cycle rather than at merge will partner with Maps Client Quality Engineering leads and SDETs SWE platform teams Maps Eval Release Engineering and Apples AI/ML platform organization.

The Quality Engineering Intelligence (QEI) team owns how Maps Client Quality Engineering standardizes AI integration across the organization. We build the shared platform set the patterns and work alongside QE Leads SDETs and engineering managers to bring AI capabilities into the workflows they run every day so AI adoption happens in a consistent supported way rather than as one-off experiments scattered across teams. nnOur mandate is to keep the organization ahead of where the industry is going on AI engineering. We evaluate emerging models agents and tooling patterns as they appear harden the ones that prove out into reusable building blocks and graduate field-tested work back into the platform so the rest of the team can build on top of thrives in this rolenn- Engineers who want to own a product end-to-end discovery build ship adoption rather than specialize in one - Engineers who enjoy understanding how other engineers work and what would change their day and who treat that discovery as part of the job rather than a handoff from someone - Engineers comfortable starting from observation rather than a written spec and revising direction as they - Engineers with applied AI experience who treat the model as a tool not the product. You ship Python and TypeScript every week and reach for an LLM only when it is the right - Engineers who stay calm under release pressure and surface risks early before they become - Engineers who communicate clearly across engineering teams leadership and the QE org they are embedded

Stay close to the users. Build a working understanding of how a triage lead handles a presubmit sessionhow a release readiness review runs where SDETs lose time on flaky tests. Earn that understanding through review attendance shared channels and one-on-ones not by waiting for a what to build. The most consequential decision is what to take on next. Most ideas will not make the cut. Picking well and being willing to drop work that is not landing is the core of the end-to-end. Python services (FastAPI aiohttp) and TypeScript front-ends RAG pipelines agents and MCP integrations. End-to-end includes auth deployment observability and the rollback sound trade-offs. Choose between scope speed and quality under release pressure. Adjust plans early to protect delivery rather than late to explain a hands-on in the code. Read and review across services and front-ends. Step in directly when progress or clarity depends on it even on code you do not with the work after launch. Adoption is the deliverable. After a release cycle the question is who is using it and how often. If the answer is no one you go back and find out what works. Patterns proven in the field become shared building blocks agents skills MCP servers and services that other tools depend on. You own that path back to the

3 years shipping production software end-to-end backend frontend or both used by real users(internal or external).nnStrong Python and TypeScript. You can move between FastAPI / aiohttp and without a specific recent example of a tool or feature you built that another team picked up unprompted you can describe the team the problem and how adoption played out in terms consistent with your prior confidentiality specific recent example of a feature you decided not to build and the reasoning behind it. Demonstrated engineering judgment in scoping including descoping or deprecating features that did not meet adoption working from observation rather than from a written spec. You have done at least one project where you started by learning the workflow first not by reading a knowledge of LLM application building you have shipped something using an LLM API written prompts that mattered and debugged a retrieval-augmented system that was returning the wrong can describe one time you removed or retired a feature you personally built because adoption did not justify keeping or Masters degree in Computer Science or equivalent with 3-6 years of industry experience in software development.

Experience as a Forward Deployed Engineer Solutions Engineer Applied AI Engineer internal tools engineer or developer experience engineer any role where you owned both the build and the have built and shipped MCP servers agents or RAG systems against an internal knowledge in QE developer tooling internal platforms or test infrastructure XCTest / XCUI experience is a plus but workflow understanding matters more than framework with Apple-internal engineering platforms (Radar Stash Arches Twist) or a track record of getting fluent in unfamiliar enterprise tooling with vector databases (LanceDB Milvus Pinecone) event-driven systems (Redis RQ Celery) and containerized deployments (Docker Kubernetes / Helm).nnA public artifact a tool an internal post a talk a write-up that demonstrates how you think not just what you can do.