Jobs

AIML - Sr Machine Learning Engineer - Answers, Knowledge & Information
Santa Clara, CA
·
On-site
·
Full-time
·
1mo ago
Benefits & Perks
•Remote work flexibility
•Wellness benefits
•Learning and development stipend
•Parental leave program
•Flexible PTO policy
Required Skills
PyTorch
Airflow
SQL
Job Description
About the Role
Do you want to make Siri and Apple products smarter for our users? The Answers, Knowledge & Information team is redefining how hundreds of millions of people use their devices to get information. We are an Applied ML team pushing the limits of apple intelligence, assistant response ranking, and search technologies, while also responsible for a production service. We are part of a wider effort to power information across a variety of Apple products - including Siri, Spotlight, Safari, Messages, Lookup, and more. In our team, you will be leveraging and improving upon the latest LLM/ML techniques in order to understand queries, rank user intents, rank documents, and generate answers to users' questions. Our team is responsible for training and deploying these models at scale, using the latest advances for online inference optimization.
As a member of our fast-paced group, you'll have the unique and rewarding opportunity to shape upcoming products from Apple. Our team includes a diversity of backgrounds from applied machine learning engineers with a focus on ML and LLM to experienced distributed systems engineers. As such, we are looking for candidates with applied machine learning experience and strong software engineering skills.
Responsibilities
- Build world class large language model at scale to power question and answering system for Siri
- Craft and develop core software infrastructure to support natural language understanding system in production
- Design and implement the low latency and high reliability runtime tech stacks for global search
- Design and develop data pipeline and infrastructure for large scale of data processing
- Evaluate and benchmark Apple natural language processing models, and deploy them to serve billions of Apple users
Minimum Qualifications
- 8+ years of industry related experience, working in collaborative environments
- Utilizing Py Torch, Tensor Flow, and JAX for training and deploying deep learning models
- Understanding product requirements and then translating them into modeling tasks and engineering tasks
- Building models for search relevance ranking, query understanding, and summarization
- BS or MS in Computer Science or related field
Preferred Qualifications
- PhD in Computer Science, Artificial Intelligence, Machine Learning, Information Retrieval, Data Science or related field
- Experience in RAG, LLM Reasoning, and LLM Agent
- Experience working in a complex organization with multiple collaborators, strong track record in scaling and launching projects in this setting
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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