Forward Deployed Engineer

Imagine what you could do here. At Apple, new ideas have a way of becoming outstanding products, services, and customer experiences very quickly. Bring passion and dedication to your job, and there"s no telling what you could accomplish.\\n\\nApple"s Sales organization generates the revenue needed to fuel our ongoing development of products and services. This, in turn, enriches the lives of hundreds of millions of people around the world. We are, in many ways, the face of Apple to our largest customers.\\n\\nApple"s US Decision Intelligence (DI) team is looking for a talented individual who is passionate about crafting, implementing, and operating AI solutions that have a direct and measurable impact on Apple Sales and its customers.

We"re looking for a Forward Deployed Engineer (FDE) who thrives at the intersection of business and engineering — someone who can sit shoulder-to-shoulder with Sales teams, translate ambiguous business problems into shippable code, and bring those learnings back to shape the roadmap of our internal AI-driven insight platform. This role is the connective tissue between Sales, our data science team, Product Management, and the software and AI engineers building the platform. You will spend your time roughly evenly between embedded user engagement and hands-on engineering, building prototypes that solve real Sales problems today and influencing what becomes a first-class platform capability tomorrow.

Serve as the primary technical bridge between Sales teams and the product and engineering organization, translating frontline business problems into engineering specifications and shippable solutions.\\nEmbed directly with Sales teams to understand their workflows, gather actionable signal on platform gaps, and become the trusted technical partner they reach for first.\\nBuild prototypes, demos, and reference implementations on top of the platform that solve concrete Sales problems quickly, then distill the most valuable patterns into reusable abstractions that scale our speed and quality of delivery — partnering with Product and Engineering to graduate them into first-class platform features.\\nPartner closely with the data science team to operationalize novel models, scoring algorithms, and analytical workflows into scalable, production-grade services.\\nPartner with the software and AI engineering teams to integrate Sales-facing solutions with platform APIs, SDKs, and shared services — championing the shift from one-off consulting work to self-service adoption.\\nAct as the voice of the user — funnel deep, structured insights on API quality, developer experience, and unmet needs back to Product Management and Engineering leadership to inform roadmap timing and prioritization.\\nBuild internal community around the platform: author tutorials, onboarding materials, and demos that help Sales users and partner engineers get the most out of it.\\nPartner with Data Science, Software and AI Engineering, and Product Management to connect high-level strategy to day-to-day execution, aligning teams without direct authority.\\nCommunicate status, trade-offs, and outcomes clearly to business partners, engineering teams, and leadership across the full lifecycle of every engagement.

5+ years of experience in Forward Deployed Engineering, Solutions Architecture, full-stack product engineering, or a related highly technical, cross-functional role.\\nDemonstrated customer obsession and product thinking — ability to act as a technical partner to internal customers and translate vague requirements into concrete engineering specifications.\\nStrong full-stack engineering skills, with proficiency in Python and JavaScript/Node.js, and the ability to ship working prototypes end-to-end across backend, data, and frontend layers.\\nFamiliarity with SQL and relational databases (e.g., PostgreSQL, Snowflake) and the ability to navigate analytics workflows and data pipelines.\\nFunctional literacy in AI/ML concepts — you understand the lifecycle of LLM-powered systems (prompts, retrieval, evaluation, inference) and can discuss the engineering trade-offs involved.\\nDemonstrated experience partnering with applied scientists, data scientists, or researchers — you can navigate the ambiguity of research-style workflows and operationalize prototype code into production-grade services.\\nWorking knowledge of REST APIs, GraphQL, microservices, and distributed systems architectures.\\nComfortable leveraging AI-assisted development tools (e.g., Claude Code) to accelerate prototyping, code generation, and documentation, and able to critically review and validate AI-generated output before it ships.\\nExceptional written and verbal communication skills, with the ability to represent the platform to executive leadership, partner teams, frontline Sales users, and the broader engineering community.\\nDemonstrated ability to navigate extreme ambiguity, define roadmaps where none existed, and influence without direct authority.\\nAbility to work in a fast-paced, dynamic, constantly evolving business environment.\\nB.S. degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience.

Experience supporting Sales, Operations, and Finance stakeholders, and a track record of translating commercial workflows into software.\\nHands-on experience building or integrating LLM-powered or agentic AI applications, including prompt design, retrieval pipelines, and evaluation harnesses.\\nFamiliarity with data science tooling such as Dataiku, Snowflake, Airflow, or Python-based analytics pipelines.\\nExperience with full-stack web frameworks, including Node.js/Express.js, Apollo GraphQL, and React or similar frontend technologies.\\nExperience designing tools, SDKs, or APIs with self-service adoption as a first-class constraint — championing the transition from a consulting model to a self-service model.\\nHands-on experience with containerized environments using Docker and Kubernetes.\\nFamiliarity with observability and tracing tools such as Langfuse, PagerDuty, or equivalent LLM call tracing platforms.\\nComfortable shipping prototype code that may be replaced by platform features as patterns mature.\\nA background in bridging research-heavy environments with production engineering teams.\\nAdvanced Degree (MS) in Computer Science, Engineering, Data Science, or a related technical field is preferred.