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Senior ML Engineer, Data Innovation and Operations - Special Projects
Santa Clara, CA
·
On-site
·
Full-time
·
1mo ago
Benefits & Perks
•Annual team offsites
•Remote work flexibility
•Learning and development stipend
•Flexible PTO policy
•Wellness benefits
•Health, dental, and vision coverage
Required Skills
Apache Spark
Airflow
SQL
ML Data Engineer
About the Role
Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other's ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you'll do more than join something - you'll add something.
At Apple, we strive to make great products that empower people and improve their lives. We believe recent breakthroughs in AI/ML and robotics have the potential to unlock new experiences that were never possible before, and are looking for talented ML data engineers to realize that mission by expanding the capabilities of Apple products and platforms.
As a member of the team, you'll have the opportunity to work with a team of highly skilled engineers and scientists to bring new experiences to Apple products. This position requires a self-motivated ML data engineer with strong technical and interpersonal skills.
Responsibilities
- Define data acquisition strategies to align with program goals. Balance cost, scalability, and quality tradeoffs, and adapt the data strategies
- Define data requirements in partner with cross functional teams
- Design and implement tools for monitoring, alerting, and health checking to ensure sustained data and model performance at scale
- Own data operations and ensure data delivery on time with the required quality
- Drive experimentation and statistical analysis to uncover actionable insights and inform decision-making
Minimum Qualifications
- B.S. in Computer Science, Math, Machine Learning, Robotics, or related fields
- Minimum of 3 years relevant industry experience
- Proficiency in SQL and Python
- Familiarity with modern data platforms and ML ops tooling (Spark, Kubeflow, Weight & Bias)
- Experience working with large-scale data pipelines
- Experience building datasets for ML systems
- Experience in data operation, budgets, vendors, or external partnerships
Preferred Qualifications
- M.S. or Ph.D. in related fields
- Experience working with multi-modal foundation models
- Experience leading and mentoring engineering teams
- Familiarity with robotic algorithms
- Familiarity with modern ML approaches
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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