Applied LLM Research Engineer

From our origins in iPhone keyboard input, the Input Experience NLP team has

expanded our broad charter: enhancing the user experience with robust language

understanding and personalized text composition, across all Apple platforms and

languages. Generative AI is a transformative technology, and we are just

beginning to harness its potential to help users digest information and express

themselves more clearly. On our team, you will help build the future and shape

its evolution. Our work has been featured in multiple WWDC keynotes including

Intelligent Input in 2023, Writing Tools, Summarization, Smart Reply as part of

Apple Intelligence in 2024! Building on years of innovation in intelligent

systems and on-device machine learning, we are now scaling efforts in bringing

powerful foundation models directly into everyday workflows. We are looking for

Applied LLM Research Engineer to innovate and develop technology that brings

powerful foundation models directly into everyday user workflows, across

languages, writing styles, and personal context, in a privacy-preserving way.

You will build, run, and refine the training and evaluation pipelines that

define our slice of Apple Intelligence, driving the experimentation and

iteration that makes the user experience feel magical. You will join an

ambitious, collaborative team in a unique position to bridge the gap between

cutting-edge ML research and features used by millions. You will work closely

with cross-functional partners in human interfaces, user studies,

internationalization, and system integration. You are not just developing

technology; you are crafting experiences that feel like magic to the end user.

Apple is where individual imaginations gather together, committing to the values

that lead to great work. Every new product we build, service we create, or Apple

Store experience 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.

DESCRIPTION

As an Applied LLM Research Engineer, you will enable next-generation AI

applications using Apple Foundation Models. You will sit at the intersection of

cutting-edge research and product reality, bridging the gap between raw model

performance and the nuanced needs of Apple customers worldwide. You will

explore, design, and implement emerging techniques, ensuring alignment with

product goals, privacy requirements, and performance metrics. You will

contribute to all phases of model development: problem formulation,

experimentation, evaluation, fine-tuning, and continuous improvement. Finally,

you will help define and refine new features that expand both the depth of Apple

Intelligence’s capabilities and the breadth of its support for our global

customer base.

MINIMUM QUALIFICATIONS

PhD in CS/EE/Physics/Statistics/etc.; or Bachelor’s or Master’s in

CS/EE/Physics/Statistics/etc combined with 2 years of relevant experience Strong

foundations in ML & LLM, including core principles, techniques and practical

applications Familiarity with post-training techniques such as SFT, RLHF, data

synthesis, Parameter-Efficient Fine-Tuning Familiarity with training frameworks

such as PyTorch, JAX, TensorFlow, or equivalent

PREFERRED QUALIFICATIONS

Experience with fine-tuning and deploying large ML models for real world

products Experience curating, filtering, and synthesizing high-quality training

datasets at scale Experience developing and training models for agentic

workflows, tool calling and advanced reasoning techniques Experience with

training LLMs with RLVR, reward modeling, environment design Familiarity with

training for computer-use capabilities Familiarity with designing

hardware-efficient model architectures, optimizing inference latency, and

implementing advanced decoding strategies, speculative decoding Active

contributor to complex, large-scale codebases, with a strong emphasis on writing

high-quality, maintainable, and well-tested code. Experience using AI-assisted

development tools (e.g., Claude, Copilot, or similar) to accelerate

experimentation, code development, and research workflows Excellent programming

and communication skills