Jobs
Benefits & Perks
•Remote work flexibility
•Learning and development stipend
•Flexible PTO policy
•Annual team offsites
•Health, dental, and vision coverage
Required Skills
Python
TensorFlow
Airflow
About the Role
Video is at the core of nearly all Apple products, and as an engineer on our team, you will research practical ML algorithms and build with us the next generation of video technology. We are looking for a highly ambitious individual, who will flourish working on technically meaningful problems at the frontier of deep learning and compression research. Your work will redefine the video experience for billions of users.
In this role you will work together with colleagues to develop ML-based video approaches for current and future Apple products. This position requires a highly self-directed individual, who is comfortable working at the cutting edge of research. Strong and analytical skills will be critical towards solving challenging problems in uncharted technical territories.
Responsibilities
- Research various components of a DL-based approach to image/video processing problems: invent models, design appropriate datasets and pipelines to train them, prototype new architectures to improve performance, study existing literature, and so on.
- Collaborate closely with team members to optimize the efficiency of the models and deploy them on particular hardware architectures.
- Work with cross-functional teams to achieve quality and computational requirements towards shipping the technology.
- Communicate: demonstrate the technology, present to leadership, discuss progress with colleagues, and so on.
Minimum Qualifications
- Master's degree in Computer Science, Electrical Engineering, or closely related fields.
- 5+ years of hands-on experience in ML research, which can include Ph.D. work.
- Proven track record of success in deep learning, with publications in top ML/CV venues.
- Familiarity with the latest ML and CV innovations.
- Software skills in common ML tools such as Py Torch or Tensor Flow.
- Knowledge of ML and specifically deep learning: principles, model prototyping and architecture design, training procedures, visualization and debugging methodology, objective function design, and so on.
Preferred Qualifications
- Ph.D. in Computer Science, Electrical Engineering, or closely related fields.
- Experience implementing custom ops in CUDA.
- Experience in designing practical ML implementations and having ML experience in industry.
- Experience with generative modeling, optical flow, perceptual quality, ML for video or data compression.
- Excellent written and oral communication skills.
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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