Agent Sciences and Infrastructure Engineer, AIML

The Apple Intelligence Agents, Infrastructure, and Research team brings innovative AI research into Apple products, with a focus on optimizing, interpreting, and developing new algorithms for on-device and server-based Apple Foundation Models and Apple Intelligence features.

## Description

We are looking for talented engineers to build the infrastructure and agentic systems that are used to prototype, optimize, and ship Apple Intelligence features. You will join a collaborative team of software developers and applied scientists focused on large language modeling, agent harnesses, evaluation, and the pipelines that connect training, adaptation, and deployment. Successful candidates will bring a strong software and systems engineering background, hands-on experience building ML or agent infrastructure end to end, and the judgment to make pragmatic tradeoffs between velocity and long-term maintainability.

## Minimum qualifications

Independently scope and lead complex, multi-month engineering projects, from an ambiguous starting point through to a production system

Proven ability to define goals and deliver results amid uncertainty and real-world constraints in AI product development

Experience designing, building, and operating infrastructure for machine learning - training/evaluation pipelines, data and experiment tooling, serving, or agent harnesses

Strong software engineering fundamentals: distributed systems, APIs, testing, and reliable, maintainable code

Strong Python and UNIX skills and a demonstrated ability to use agentic coding tools in these environments

Proven track record of driving engineering efforts to completion while overcoming obstacles and ambiguity

BS and 5+ years of experience, MS and 3+ years of experience, or PhD and 1+ year of experience

## Preferred qualifications

Experience building agent frameworks, tool-use systems, or LLM evaluation infrastructure

Experience with data standardization, validation, and lineage for ML pipelines at scale

Familiarity with post-training or optimizing large language models (LLMs) and the workflows around them

Experience shipping a real-world product, project, or feature

Experimental rigor and sound benchmarking/observability practices for complex systems

Ability to reproduce and identify critical bottlenecks in the latest research and translate it into robust tooling

Strong communication and accountability skills, with a collaborative mindset and strong work ethic