Forward Deployed Engineer

Job description

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.

Apple'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.

Apple'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.
<h3>Minimum Qualifications</h3>8+ years of experience in Forward Deployed Engineering, Solutions Architecture, full-stack product engineering, or a related highly technical, cross-functional role.
Demonstrated customer obsession and product thinking — ability to act as a technical partner to internal customers and translate vague requirements into concrete engineering specifications.
Strong 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.
Familiarity with SQL and relational databases (e.g., PostgreSQL, Snowflake) and the ability to navigate analytics workflows and data pipelines.
Functional 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.
Demonstrated 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.
Working knowledge of REST APIs, GraphQL, microservices, and distributed systems architectures.
Comfortable 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.
Exceptional 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.
Demonstrated ability to navigate extreme ambiguity, define roadmaps where none existed, and influence without direct authority.
Ability to work in a fast-paced, dynamic, constantly evolving business environment.
B.S. degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience.
<h3>Preferred Qualifications</h3>Experience supporting Sales, Operations, and Finance stakeholders, and a track record of translating commercial workflows into software.
Hands-on experience building or integrating LLM-powered or agentic AI applications, including prompt design, retrieval pipelines, and evaluation harnesses.
Familiarity with data science tooling such as Dataiku, Snowflake, Airflow, or Python-based analytics pipelines.
Experience with full-stack web frameworks, including Node.js/Express.js, Apollo GraphQL, and React or similar frontend technologies.
Experience 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.
Hands-on experience with containerized environments using Docker and Kubernetes.
Familiarity with observability and tracing tools such as Langfuse, PagerDuty, or equivalent LLM call tracing platforms.
Comfortable shipping prototype code that may be replaced by platform features as patterns mature.
A background in bridging research-heavy environments with production engineering teams.
Advanced Degree (MS) in Computer Science, Engineering, Data Science, or a related technical field is preferred.