招聘
Benefits & Perks
•Healthcare
•401(k)
•Equity
•Learning Budget
•Healthcare
•401k
•Equity
•Learning
Required Skills
Data Engineering
Software Engineering
Technical Leadership
Do you believe Machine Learning and AI can change the world? We truly believe it can! We are the ML Data Ops team at Apple. We build high quality ML datasets, from very small targeted sets to petabyte scale, to train and evaluate ML models that power AI-centric features for many Apple products. Our datasets help power intelligent algorithms on Camera and the Photos app, improve text input experiences (autocorrect, autocompletion, OCR) and more recently feed generative technologies in Apple Intelligence (Image Playground, Genmoji, Writing Tools, Math Notes...)
We're looking for an exceptional engineering lead who is passionate about Apple products and values; who loves working with data ops at scale; and who is committed to the hard work vital to continuously improve complex ML data pipelines and data infrastructure. AI-centric products are the future of software; at their core, data is the source code of AI, a key component of innovation and inclusive and fair ML products. We invite you to join us at this exciting time; grow fast and positively impact multiple critical features on your first day at Apple!
Description:
Our Data team focuses on acquiring, synthesizing, annotating, and ensuring the quality of ML data, driving numerous features in collaboration with R&D teams in Apple's SWE organization. As a data engineering tech lead, you will be responsible for establishing and executing the strategy for our organization's ML data engine.","responsibilities":"Collaborate with a variety of partners, from infrastructure, ML research teams to our data functions, including data engineers, to assess the needs
Identify state of the art data components used to store, expose and track ML data
Identify opportunities for improvement of internal infrastructure offerings, and influence roadmaps of partner teams to build or improve key components we rely on
Design and execute the roadmap for adoption of new components, build the pipelines necessary to connect data systems and teams
Improve automation workflows, data visualization tools, ML enrichment, asset lineage tracking, including data coming from sophisticated synthetic workflows, data governance and compliance components, storage and tracking of synthetic data
Be hands on, actively participate to the stack and implement high quality code
Preferred Qualifications:
Proven experience designing, automating and scaling large data pipelines (petabyte scale desired) using state of the art technologies
Expertise in Python or another modern programming language
Strong ability to design and lead a technical roadmap, work with cross functional teams and a diversity of profiles, proven capacity to influence and build alignment
Demonstrated prior experience in foundation models such as building data infrastructure supporting generative technologies
Self-starter, able to handle ambiguity, identify risks, mitigate, and autonomously find the right people and tools to get the job done
Proven track record of mentoring and growing engineers, lift the level of software engineering excellence in a ML data ops team typically focused on short term deliveries
Minimum Qualifications:
Bachelors, Masters or PhD in Computer Science, Mathematics, Physics, or a related field; or equivalent practical experience.
7+ years of industry experience as a software engineer, including 2+ years as a tech lead/architect specialized in data infrastructure
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 $181,100 and $318,400, 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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About Apple

Apple
PublicA technology company that designs, manufactures, and markets consumer electronics, personal computers, and software.
10,001+
Employees
Cupertino
Headquarters
$3.5T
Valuation
Reviews
4.0
10 reviews
Work Life Balance
4.0
Compensation
4.2
Culture
3.8
Career
3.5
Management
3.2
75%
Recommend to a Friend
Pros
Great coworkers and people
Excellent benefits and perks
Fast-paced and engaging work environment
Cons
High expectations and pressure
Management quality varies
Limited career progression opportunities
Salary Ranges
17,968 data points
Junior/L3
L2
L3
L4
L5
L6
M3
M4
M5
M6
Principal/L7
Senior/L5
Staff/L6
Junior/L3 · Data Scientist ICT2
0 reports
$121,979
total / year
Base
-
Stock
-
Bonus
-
$103,682
$140,276
Interview Experience
5 interviews
Difficulty
3.4
/ 5
Duration
28-42 weeks
Offer Rate
20%
Experience
Positive 20%
Neutral 40%
Negative 40%
Interview Process
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Behavioral Interview
5
Onsite/Virtual Interviews
6
Team Matching
7
Offer
Common Questions
Coding/Algorithm
System Design
Behavioral/STAR
Technical Knowledge
Culture Fit
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