Applied LLM Research Engineer, Input

Position: Applied LLM Research Engineer, Input Experience
** Role Number:*
* ** Summary*
* 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, Tensor Flow, 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…