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Benefits & Perks
•Wellness benefits
•Remote work flexibility
•Parental leave program
•Learning and development stipend
Required Skills
PyTorch
Apache Spark
SQL
About the Role
We're seeking engineers excited about applying machine learning to build and maintain the core ML products behind Apple's speech technologies. You'll join a highly technical, collaborative team of ML, software, and infrastructure experts to develop systems used by millions of people. This role offers the chance to work on impactful projects across Apple and to apply your ML, data science, and analytical skills to solve complex problems and deliver innovative user-facing products.
Join a pivotal team that builds the automation and scalable infrastructure that carries Apple's speech research into production for millions of users. We design and maintain the training and evaluation pipelines that enable rapid experimentation, robust model validation, and seamless deployment of next-generation speech technologies.
In this role, you will engineer and refine the systems that transform research ideas into reliable, production-ready modeling workflows. You'll improve automation, ensure high-quality model behavior at scale, and create the frameworks that accelerate model development across the speech organization. Your work forms the backbone of Apple's state-of-the-art speech capabilities—powering technologies that reach our users around the world.
Responsibilities
- Build and maintain scalable pipelines for training and evaluating speech models for fast and reliable experimentation and for production
- Integrate new model architectures, training recipes, and evaluation methods into existing systems
- Monitor pipeline health and model performance, helping identify regressions or data issues
- Collaborate with modeling and infrastructure teams to improve reliability and efficiency
- Contribute to tooling that supports experiment tracking, reproducibility, and analysis
- Document workflows and follow best practices for ML software development
Minimum Qualifications
- Experience designing, building, and maintaining scalable ML pipelines for training, evaluation, and continuous monitoring
- Strong software engineering skills, with proficiency in Python
- Solid understanding of core ML concepts, such as supervised/unsupervised learning, model evaluation, and performance analysis
- Strong verbal and written communication skills
- Bachelor's or graduate degree in Computer Science, Computer Engineering, or a related field, or equivalent experience
Preferred Qualifications
- Hands-on experience with distributed compute and data systems (Spark, Ray)
- Working knowledge of speech/ASR/TTS concepts (audio features, tokenization, speech augmentation, alignment, evaluation metrics)
- Comfort working cross-functionally with research scientists, data engineers, and product teams
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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