Data Scientist - Intelligent Experiences, MLO

Do you believe machine learning and AI can change the world? We do. We're the ML Data team within Apple's Intelligent System Experience (ISE) group, and we build the high-quality, high-scale datasets that train the ML features across many Apple products - iPhone, iPad, Mac, Apple Watch, and AirPods. Our work sits behind some of the most visible and personal experiences Apple ships: the wallpaper on your Lock Screen, personalized stickers in iMessage, on-device Genmoji and other Apple Intelligence generative models, and models that recognize faces and build memories of the people you love in Photos.

We're looking for a data scientist who's passionate about ML data and excited to shape the training data behind Apple's next generation of multi-modal features. The ideal candidate thrives in a fast-paced environment, brings rigorous scientific reasoning and owns problems end-to-end.

Join us and you’ll shape shipping products from your first week, working with petabytes of multi-modal data.

Description

You will design and operate the end-to-end data lifecycle that supports training, fine-tuning, and evaluation of large language models and diffusion models. Day to day, that means building distributed pipelines that clean, deduplicate, and enrich massive multi-modal datasets; standing up synthetic data generation pipelines with LLM-based generators, validators, and judges; and engineering agentic workflows - multi-step, tool-using LLM systems - that scale human judgment across far more data than a human team could review directly.

Minimum Qualifications

Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related engineering field

5+ years of hands-on experience applying NLP, computer vision, or multimodal machine learning to production systems, including experience with large-scale data pipelines

Preferred Qualifications

Experience prompting, fine-tuning, and evaluating LLMs or vision-language/diffusion models for specific downstream tasks

Experience with parameter-efficient fine-tuning (LoRA/QLoRA), preference optimization (RLHF, DPO), RAG and retrieval-based data curation

Experience designing, evaluating and productionizing data science algorithms used for data curation, data filtering, deduplication and data down-selection.

Experience designing controlled experiments or ablation studies to understand effect of data on model quality

Experience building agentic or multi-agent, tool-using LLM systems and human-in-the-loop workflows, calibrating judges against human labels, and mitigating failure modes

Strong software engineering skills, with 5+ years in Python, workflow orchestration (Airflow), cloud platforms (AWS, GCP), distributed data processing frameworks (Spark, Ray, or similar) at large scale

Publication record or open-source contributions in NLP, computer vision, or generative AI is a plus

Familiarity with privacy-preserving data techniques (e.g., differential privacy, on-device data constraints) is a plus