Senior AI Software Engineer - App Intelligence

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.

Minimum Qualifications

Bachelor's degree or foreign equivalent in Computer Science, EE, Applied Mathematics, or related field with 6+ years experience in developing ML / AI driven software. Proficiency in one or more of the following programming languages: Python, Swift, Objective-C, or C++ Experience developing software prototypes and features driven by Large Language Models and designing tool and function-calling interfaces, constructing context, and validating model output Experience developing and evaluating ML Models and Algorithms using technologies such as a Python, PyTorch, SQL, and visualizing results via dashboards built using tools like Tableau Fluency with agentic coding tools like Claude Code 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

Preferred Qualifications

Experience designing and developing systems that are efficient in resource constrained environments — latency, memory, and power budgets on device 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 Proven ability to excel in a fast-paced development team

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.

Responsibilities

Lead the design and development of software features that enable end to end app experiences in Siri Develop ML models and AI driven algorithms and approaches for app intelligence to make apps better and more capable with Siri Run experiments at scale on complex datasets and scenarios and evaluating models and algorithms for app intelligence and Siri integration Researching and evaluating new technologies in ML and AI and help design modeling approaches that work in a performant manner both on-device and cloud 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 Help inform architectural choices to be made for the software stack to support ML and AI features and help integrate these features in the software stack 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