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 theres no telling what you could 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 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.

Were 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 directly with Sales teams to understand their workflows gather actionable signal on platform gaps and become the trusted technical partner they reach for 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 closely with the data science team to operationalize novel models scoring algorithms and analytical workflows into scalable production-grade 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 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 internal community around the platform: author tutorials onboarding materials and demos that help Sales users and partner engineers get the most out of with Data Science Software and AI Engineering and Product Management to connect high-level strategy to day-to-day execution aligning teams without direct status trade-offs and outcomes clearly to business partners engineering teams and leadership across the full lifecycle of every engagement.

8 years of experience in Forward Deployed Engineering Solutions Architecture full-stack product engineering or a related highly technical cross-functional customer obsession and product thinking ability to act as a technical partner to internal customers and translate vague requirements into concrete engineering full-stack engineering skills with proficiency in Python and JavaScript/ and the ability to ship working prototypes end-to-end across backend data and frontend with SQL and relational databases (e.g. PostgreSQL Snowflake) and the ability to navigate analytics workflows and data literacy in AI/ML concepts you understand the lifecycle of LLM-powered systems (prompts retrieval evaluation inference) and can discuss the engineering trade-offs 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 knowledge of REST APIs GraphQL microservices and distributed systems 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 written and verbal communication skills with the ability to represent the platform to executive leadership partner teams frontline Sales users and the broader engineering ability to navigate extreme ambiguity define roadmaps where none existed and influence without direct to work in a fast-paced dynamic constantly evolving business .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 -on experience building or integrating LLM-powered or agentic AI applications including prompt design retrieval pipelines and evaluation with data science tooling such as Dataiku Snowflake Airflow or Python-based analytics with full-stack web frameworks including Apollo GraphQL and React or similar frontend 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 -on experience with containerized environments using Docker and with observability and tracing tools such as Langfuse PagerDuty or equivalent LLM call tracing shipping prototype code that may be replaced by platform features as patterns background in bridging research-heavy environments with production engineering Degree (MS) in Computer Science Engineering Data Science or a related technical field is preferred.