Jobs

AIML - Sr. Machine Learning Engineer - ML Platform Technologies (MLPT)
Seattle, WA
·
On-site
·
Full-time
·
1mo ago
Benefits & Perks
•Learning and development stipend
•Parental leave program
•Flexible PTO policy
•Wellness benefits
•Annual team offsites
Required Skills
SQL
PyTorch
Airflow
Machine Learning Engineer
- ML Platform
Join us in enabling the next generation of intelligent experiences in Apple's products and services with the latest advancements in Generative AI and Large Language Models!
We are looking for a very experienced, machine learning engineer, who has also worked in ML infrastructure. Ideally, you should have experience with shipping intelligent features, end-to-end, and from idea to production. You should also have experience in building ML infrastructure to enable others to iterate faster and expertise in certain ML domains (preferably in Gen AI and LLM). You will help find novel solutions for our ML problems and collaborate with all ML teams at Apple to optimize our platform.
Are you ready to impact billions of our users?
About the Role
You'll be part of Apple's internal Machine Learning Platform group that enables ML teams to iterate their projects faster, from: data engineering, labeling, training, online and offline evaluation, experimentation and more.
Bring your expertise to help us define and build the next set of features (given the new challenges in the latest advancement of generative AI and large language models). You will also partner closely with our customer and other internal partner ML teams cross the company to help further adopt and apply our platform, gain feedback and iterate our products (services, SDK and web UI) to be more user friendly and scalable.
Minimum Qualifications
- A minimum of 3 years as tech lead
- 8 years of proven experience in machine learning related software engineering, such as: product, infrastructure, frameworks or tools
- Strong expertise in machine learning domains, such as: CV, NLP, recommender systems, Gen AI or LLMs (preferred)
- Proficient in ML training and deployment frameworks, like: Tensorflow, Py Torch, Faster Transformer, TensorRT, vLLM
- Proficient in cloud computing and data processing infrastructure and tools: Kubernetes, Ray, Py Spark, SQL
- Ability to clearly and concisely communicate technical and architectural problems, while working with partners to iteratively find solutions
- Bachelors in Computer Science, related field or equivalent experience
Preferred Qualifications
- Advance degree
- Strong expertise in machine learning domains, such as: CV, NLP, recommender systems, Gen AI or LLMs
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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