Applied Machine Learning Engineer, Apple Intelligence

Apple

About The Position

Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build or service we create, we deliver is the result of us making each other’s ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It’s the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you’ll do more than join something — you’ll add something. As part of Apple Intelligence, you'll bring impact to billions of users through your applied ML skills, engineering rigor, and programming expertise. You will work alongside a dedicated team of ML and software engineers, applying and integrating generative modeling techniques into production systems. Our team focuses on generative technologies for key international markets, and you'll help own components across the pipeline, from data preprocessing to evaluation and deployment, ensuring our systems are culturally and linguistically adaptable across a range of local experiences. You are passionate about coding and software development lifecycle methodologies, with a strong focus on debugging and performance. The specific platforms, frameworks, and components will change over time, so we're looking for someone who can transition smoothly across them and bring strong evaluation and systems engineering fundamentals to whatever the team needs next. If you're an engineer with strong applied ML who enjoys building generative AI systems that reach real users at scale, this is a great opportunity to make a meaningful impact. Product Engineering International, part of Apple's Software Engineering organization, is seeking an accomplished Applied ML Engineer with proven, hands-on expertise and advanced AI systems to architect and deliver scalable, production-grade AI solutions.

Requirements

5+ years of hands-on ML experience building harnesses and integrating models into production workflows.
Proficiency in Python plus one or more of C/C++/Objective-C/Swift.
Strong software engineering foundation (OS fundamentals, OOPS).
Proven hands-on IC coder — this role is technical, not architectural — who ships clean, well-tested, maintainable code.
Strong practical use of generative-AI coding tools, specifically Claude/Cursor/Codex.
Strong debugging, systems-thinking, and problem-solving skills, demonstrated through code reviews, architectural discussions, and production troubleshooting.
Applied experience across NLP (tokenization, language modeling, decoding, classification), modern agentic and multi-agent systems, and RAG architectures.
Experience staging, provisioning, or controlling test or evaluation environments to produce repeatable, deterministic conditions.
Experience evaluating ML, LLM or agent-based systems, including familiarity with metrics, scoring methodology, or trajectory and outcome analysis.
Experience building, maintaining, or operating production-grade AI agents at scale — reliable, scalable, and safe for real-world use — iterating based on user feedback and usage data.
Good exposure to deep learning libraries such as PyTorch and TensorFlow.
Strong problem-solving skills with a drive to focus on what matters most.
Results-oriented, with a desire to work in a fast-paced environment and a commitment to excellence.
Ability to communicate effectively (written & verbal) — including sharing knowledge and elevating quality across the team — and attend to detail without losing sight of the bigger picture.

Nice To Haves

Background in developer tools, internal platforms, or productivity engineering.

Responsibilities

Applying and integrating generative modeling techniques into production systems.
Owning components across the pipeline, from data preprocessing to evaluation and deployment.
Ensuring systems are culturally and linguistically adaptable across a range of local experiences.
Debugging and performance optimization.
Transitioning smoothly across platforms, frameworks, and components.
Bringing strong evaluation and systems engineering fundamentals.
Architecting and delivering scalable, production-grade AI solutions.
Building harnesses and integrating models into production workflows.
Shipping clean, well-tested, maintainable code.
Staging, provisioning, or controlling test or evaluation environments to produce repeatable, deterministic conditions.
Evaluating ML, LLM or agent-based systems, including familiarity with metrics, scoring methodology, or trajectory and outcome analysis.
Building, maintaining, or operating production-grade AI agents at scale — reliable, scalable, and safe for real-world use — iterating based on user feedback and usage data.
Communicating effectively (written & verbal) — including sharing knowledge and elevating quality across the team — and attending to detail without losing sight of the bigger picture.