Senior AI Software Engineer, App Intelligence

Apple·California, United States·posted 28m ago · last seen 1m ago

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About interviewing at Apple

One of tech's least standardized loops: you interview for a specific team, and every stage belongs to it. Recruiter/hiring-manager screen, one to three 45–60 minute Coderpad coding screens, a system design round shaped by Apple's reliability and privacy constraints, and a behavioral round — then a panel debrief. There is no central question bank; questions map to the team's real stack.

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Description

Summary

Do you want to build the layer that lets every app on the device do its best work for the user through Siri? App Intelligence is the on-device system that turns app capabilities into things a user can simply ask for — and makes the assistant reliable enough to be trusted with them. We are engineers, scientists, and problem solvers bringing smarter, faster, and more natural interaction with apps to Siri. We are looking for seasoned AI Software Engineers to join our team and be part of this mission.

Description

In this role, you will be at the forefront of developing software on device that enables Siri to deliver on app Intelligence throughout the OS. We bring app intelligence to Siri by working directly with app teams to understand what their apps can do, modeling those capabilities so an assistant can use them well, and building the on-device software that makes the resulting experiences reliable and fast. We partner closely with the team that builds Siri's planning and execution engine, and we work the app side of that partnership: what apps can offer, how they express it, and whether the resulting experience is good enough to trust. This is early work. How apps best expose themselves to an assistant is an open question — you will help answer it by designing the interfaces, proving them out with real app partners, and iterating as we learn. Expect real ambiguity, and expect to form your own view of the right architecture and defend it with working software and measured results.

Key Responsibilities

Lead the design and development of software features that enable end to end app experiences in Siri Work directly with app teams to identify where intelligence makes their app better, and bring those capabilities to Siri Help define the shape of this layer and bring app partners along as it takes shape Implement code that is well designed, easy to debug, and highly performant Diagnose failures in end-to-end app experiences, and work with the Siri team to turn them into measurable quality improvements Contribute to the shared evaluation and telemetry loop that proves whether a change improved user outcomes Identify and propose key architecture changes to help features scale Proactively look for areas of code to further optimize for performance, and hold on-device latency, memory, and power budgets Leverage agentic coding to enhance engineering productivity, code quality, and efficiency at scale

Minimum Qualifications

4+ years experience developing and shipping production software Strong proficiency in Swift, Objective-C or C++ Experience building product features on top of large language models — designing tool and function-calling interfaces, constructing context, and validating model output Fluency with agentic coding tools for accelerating engineering productivity, with good judgment about where to apply them and how to verify what they produce Exceptional problem-solving skills, and the ability to drive alignment across teams on shared API surfaces Proven ability to excel in a fast-paced development team

Preferred Qualifications

Experience designing and developing systems that are efficient in resource constrained environments — latency, memory, and power budgets on device Experience building the scaffolding around models — tool interfaces, retrieval and context construction, output validation — with strong intuition for model behavior and failure modes Experience building key software components and APIs used cross functionally across different applications and platforms, including versioning and compatibility of interfaces that shipped clients depend on Experience with evaluation, benchmarking, and telemetry for systems whose output is probabilistic rather than deterministic

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