below marketmost similar roles pay hereabove market
This role pays more than
78%
of similar roles. Most pay
$145,087–$214,000
— the blue band above.
At the midpoint, this role pays about
$214k
versus about
$180k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
3497 open roles
on FindRole.
Listed pay typically runs
$174,149–$277,600
across 2702 roles with salary data.
The Data Scientist - Intelligent Experiences, MLO joins the ML Data team within the Intelligent System Experience group to build high-quality, high-scale datasets for multi-modal features. You will design and operate the end-to-end data lifecycle for training, fine-tuning, and evaluating large language models and diffusion models. Daily responsibilities include building distributed pipelines to clean and enrich massive multi-modal datasets, standing up synthetic data generation pipelines with LLM-based generators and validators, and engineering agentic workflows for curation and annotation. You will utilize Python, Airflow, AWS, GCP, and Spark or Ray to manage data curation, deduplication, and down-selection. The role solves technical challenges in generative AI, including prompt engineering, RAG, LoRA, QLoRA, and RLHF, to improve model quality for products like Genmoji and Photos.
What does a Data Scientist earn in California?
Median $214500 from 114 postings across 26 companies.
Design and operate the end-to-end data lifecycle for training, fine-tuning, and evaluating large language and diffusion models.
Build distributed pipelines to clean, deduplicate, and enrich massive multi-modal datasets.
Develop synthetic data generation pipelines using prompt engineering, generator/critic loops, and diversity controls.
Engineer agentic workflows and LLM-based flows for curation, annotation, labeling QA, and evaluation.
Define and run quality evaluations for synthetic assets using perceptual metrics, VLM judges, and human preference studies.
Fine-tune and evaluate models for specific downstream tasks using controlled ablations to isolate data interventions.
Track emerging techniques in data curation and generative AI to provide evidence-based adoption recommendations.
What we're looking for
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.
Experience with large-scale data pipelines.
Experience prompting, fine-tuning, and evaluating LLMs or vision-language/diffusion models for specific downstream tasks (preferred).
Experience with parameter-efficient fine-tuning (LoRA/QLoRA), preference optimization (RLHF, DPO), RAG, and retrieval-based data curation (preferred).
Experience designing, evaluating, and productionizing data science algorithms for data curation, filtering, deduplication, and down-selection (preferred).
Experience designing controlled experiments or ablation studies to understand the effect of data on model quality (preferred).
Experience building agentic or multi-agent, tool-using LLM systems and human-in-the-loop workflows (preferred).
5+ years of experience in Python, workflow orchestration (Airflow), cloud platforms (AWS, GCP), and distributed data processing frameworks (preferred).
Publication record or open-source contributions in NLP, computer vision, or generative AI (preferred).
Familiarity with privacy-preserving data techniques (preferred).
Python
SQL
LLM
NLP
Statistics
Data Manipulation
Natural Language Processing
Metrics Development
Exploratory Data Analysis
Machine Learning
Data Science