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Imagine shaping how millions of people discover content they love on the App Store, Apple Music, and Apple TV+. Our team is responsible for the intelligence that powers these deeply personal experiences.
We are at a pivotal moment, defining the next generation of personalization. We build the foundational capabilities that empower product and research teams to deliver hyper-personalized experiences while maintaining an uncompromising commitment to user privacy. We believe that deep personalization shouldn't require compromising user trust, and we are pioneering the decentralized data systems to prove it.
Description
This is not a standard Data Engineering or ML role. We are looking for a pioneering engineer to join our team. You will build the systems that securely process, combine, and deliver the critical user and content features needed for personalization, spanning from edge devices to cloud backends. You will engineer high-performance stacks that transform raw data into governed, discoverable intelligence, ensuring that machine learning models can seamlessly and securely access the right user and content features regardless of where that data physically resides.","responsibilities":"Architect Distributed Feature Access: Design and build the access layer that abstracts the physical location of data. Ensure that inference systems can seamlessly access real-time on-device context, cloud-based service history, and content metadata through a unified, familiar API.
Engineer Large-Scale Feature Pipelines: Build robust, petabyte-scale pipelines that ingest and combine disparate data into coherent user profiles and rich content representations.
Architect Training Data Systems: Transform raw data into the high-value features that train our next-generation ML models. Architect the systems that generate this data and seamlessly integrate it with our training infrastructure.
Optimize for Privacy & Scale: Build highly optimized stacks that extend existing data systems into privacy-constrained environments. Implement data minimization strategies to securely leverage rich user features without compromising trust.
Cross-Functional Innovation: Partner closely with data systems teams, core compute engineers, and ML teams to ensure the right context is delivered to the right compute environment at the exact right time.
Preferred Qualifications
Hybrid/Edge Computing: Experience building systems that bridge cloud backend systems with on-device or edge compute environments.
Embeddings & Vector Search: Familiarity with generating, managing, and serving dense embeddings for retrieval, ranking, and personalization systems.
Data Governance: Experience building feature stores, data catalogs, or implementing compliance-by-design in a regulated environment.
Privacy-Preserving Tech: Passion for privacy and an understanding of data minimization strategies, secure enclaves, or Privacy-Enhancing Technologies (PETs).
Minimum Qualifications
BS or MS in Computer Science, Data Engineering, Software Engineering, or a related field.
Senior-Level Experience: A proven track record of shipping complex, large-scale data engineering, feature serving, or machine learning systems to production.
Mastery of Big Data & Serving: Expertise in designing distributed data processing systems using technologies like Spark and Flink, and building low-latency, high-throughput data serving layers or Feature Stores.
Strong Software Engineering: Deep proficiency in Java or Go for building high-performance production backend systems, and Python for model training ecosystems.
Strategic Data Mindset: Demonstrated experience thinking critically about data architecture, including data ontology, discoverability, and bridging distributed data sources.
Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant .
Pay & Benefits
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $139,500 and $258,100, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
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Appleについて

Apple
PublicApple Inc. is an American multinational technology company headquartered in Cupertino, California, in Silicon Valley, best known for its consumer electronics, software and online services.
10,001+
従業員数
Cupertino
本社所在地
$3.5T
企業価値
レビュー
3.9
10件のレビュー
ワークライフバランス
2.5
報酬
4.2
企業文化
3.8
キャリア
3.5
経営陣
3.2
72%
友人に勧める
良い点
Great benefits and compensation
Talented colleagues and supportive teams
Learning opportunities and mentorship
改善点
Work-life balance challenges
High stress and pressure
Fast-paced environment
給与レンジ
11,365件のデータ
Junior/L3
L2
L3
L4
L5
L6
M3
M4
M5
M6
Principal/L7
Senior/L5
Staff/L6
Junior/L3 · Data Scientist ICT2
0件のレポート
$121,979
年収総額
基本給
-
ストック
-
ボーナス
-
$103,682
$140,276
面接体験
3件の面接
難易度
3.3
/ 5
期間
28-42週間
内定率
33%
体験
ポジティブ 33%
普通 0%
ネガティブ 67%
面接プロセス
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Team Matching
6
Offer
よくある質問
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Past Experience
ニュース&話題
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