Agent Sciences and Infrastructure Engineer, AIML

Socket.dev; Cupertino (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Apple is seeking talented engineers to build the infrastructure and agentic systems for prototype, optimize, and ship Apple Intelligence features. You will join a collaborative team focused on LLMs, agent harnesses, evaluation, and pipelines connecting training, adaptation, and deployment.

Successful candidates bring a strong software and systems background, hands-on experience building ML infrastructure end to end, and the judgment to trade velocity for maintainability.

Qualifications

• Proven ability to scope and lead complex, multi-month projects from concept to production.
• Define goals and deliver results amid uncertainty in AI product development.
• Experience designing, building, and operating infrastructure for ML—training/evaluation pipelines, data/experiment tooling, serving, or agent harnesses.
• Strong software engineering fundamentals: distributed systems, APIs, testing, maintainable code.
• Strong Python and UNIX skills and experience with agentic coding tools.

Responsibilities

• Build infrastructure and agent systems used to prototype, optimize, and ship Apple Intelligence features.
• Collaborate with software developers and applied scientists on LLMs, agent harnesses, and evaluation pipelines.
• Navigate pragmatic tradeoffs between velocity and long-term maintainability in AI product development.

Skills

Python

UNIX

Distributed systems

APIs

Testing

Maintainable code

Leadership

Education

BS + 5+ years (or MS + 3+ years, or PhD + 1+ year)

Tools

Agent frameworks

LLM evaluation tooling

ML training/evaluation pipelines

Job description

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