Engineering Manager, Machine Learning & NLP, Input Experience

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 team is responsible for key Apple Intelligence portfolios

such as personalized Writing Tools, Summarization (Mail, Messages,

Notifications, etc.) , Found In, Smart Actions as well as the entire keyboard

backend: autocorrection, inline completions, proofreading, across all Apple

platforms. Building on years of innovation in intelligent systems and on-device

machine learning, we are now scaling efforts in bringing powerful foundation

models (on-device and server) directly into everyday workflows. We are looking

for an engineering manager who can work at the intersection of ML, NLP and

software engineering, specifically focused on innovating and evolving our data,

tooling, modeling and evaluation pipelines with agentic harnesses, to scale

globally. We are shifting the entire paradigm of ML product development across

feature definition, data synthesis, model training, auto-evaluation, model

probing, evaluation and user feedback to agentic workflows. You will have the

opportunity to define and execute state-of-the-art paradigm for a swathe of

high-impact features and languages, creating ML playbooks for scale, and

influencing the rest of Apple. You will also be responsible for building and

refining the personalized agent trajectory pipelines for data and evaluation

across synthetic personas and languages. The role provides an opportunity to

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.

DESCRIPTION

As an engineering manager, you will enable the next-generation of agentic ML

product development systems at scale 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 in a

hands-on role. The role requires technical depth across ML & NLP, with a solid

understanding of building scalable ML pipelines. You will redefine the ML

development process across features and languages: problem formulation,

experimentation, evaluation, fine-tuning, and continuous improvement that expand

both the depth of Apple Intelligence’s capabilities and the breadth of its

support for our global customer base.

MINIMUM QUALIFICATIONS

Masters or PhD in Computer Science, Electrical Engineering, Physics, Statistics

or related field; or equivalent practical experience Prior experience with

technical leadership or engineering management Strong foundation in Data Science

and MLOps Familiarity with product ML/NLP lifecycle Familiarity with techniques

such as SFT, RLHF, Data Synthesis, Parameter-Efficient Fine-Tuning, LLM-judge

evaluation Excellent communication skills

PREFERRED QUALIFICATIONS

Experience with deploying large ML models for real world products and leading

high-performing teams Experience curating, filtering, and synthesizing

high-quality training datasets at scale Familiarity with ML pipelines that need

to scale across languages Experience developing and training models for agentic

workflows, tool calling and advanced reasoning techniques Familiarity with

working and managing 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