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Benefits & Perks
•Top Tier compensation with equity
•Annual team offsites
•Health, dental, and vision coverage
•Parental leave program
•Wellness benefits
•Learning and development stipend
Required Skills
TensorFlow
Python
PyTorch
About the Role
Join the team responsible for Apple's Feedback and Beta Programs! Our tools and applications collect user feedback on beta software releases, helping to drive the direction and priorities of Apple's engineering teams. To make the best use of feedback we receive from customers and developers, we develop and maintain machine learning systems and automated analysis to cluster, annotate, and escalate the millions of reports we receive yearly.
We are looking for experienced Machine Learning engineers to deploy and tune both traditional ML and generative AI models to enhance our systems' capability. There are tantalizing problems and products possible with our one-of-a-kind dataset, and plenty of opportunity for a passionate engineer.
Description
Our team is seeking adept machine learning engineers who are excited by creating machine-learning-driven user experiences. Work includes implenting high-performance machine learning models and infrastructure in concert with software engineers, designers, and teams of internal users.
You will be responsible for full stack ML development in partnership with others on the team. This includes data generation and curation, creating and influencing ML tooling and infrastructure, driving evaluation efforts, and training or fine-tuning models. You will also be involved in directly integrating ML into internal tools and systems. You will need to rely on your creativity and problem solving to develop scalable, maintainable, and cost-effective solutions.
The team values rapid iteration, based on bringing models into reality so stakeholders can play with cutting edge technology and research. To this end you will be engaged with hardware, software, and design teams across Apple; both representing the technology and implementing it into prototypes.
You will be successful and feel fulfilled in our team if you enjoy tackling challenging problems, have a strong sense of shared ownership, and thrive in a collaborative team setting.
Preferred Qualifications
- Working knowledge of applied Natural Language Processing
- Strong hands-on experience in building large scale ML based solutions or in building and scaling distributed systems
Minimum Qualifications
- Industry experience with applied machine learning
- Industry experience in software engineering, data engineering, or similar
- Strong fundamentals in problem solving, algorithm design, and model building
- Excellent verbal and written communication and presentation skills
- MS in Computer Science or related experience
Equal Opportunity
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.
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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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