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