Jobs

AIML - Staff Machine Learning Engineer, Answers, Knowledge & Information (AKI)
Santa Clara, CA
·
On-site
·
Full-time
·
2w ago
Compensation
$181,100 - $318,400
Benefits & Perks
•Healthcare
•401(k)
•Equity
•Learning Budget
•Healthcare
•401k
•Equity
•Learning
Required Skills
Machine Learning
Natural Language Processing
Python
Software Engineering
The Siri and Information Intelligence team is revolutionizing the way hundreds of millions of people access information on their devices, all while keeping user privacy at the forefront. As an Applied ML team, we're pushing the boundaries of Apple Intelligence, result ranking, and innovative search technologies, all while running a low latency production service. Our work fuels intuitive information experiences across some of Apple's most iconic products, including Siri, Spotlight, Safari, Messages, Lookup, and more. Join us in shaping the future of how the world connects with information!
We are looking for a senior Machine Learning Engineer with a passion for using machine learning to build intelligent search applications. Our team researches and implements novel query understanding, ranking and response generation techniques, machine learning algorithms and models that power amazing Search experiences across Apple products.
Description:
In this role you will have the opportunity to develop LLMs and other NLP models for user productivity and improving Siri's ability to answer personal domain questions. Our team owns models which are responsible for answering users' questions using their personal documents with privacy at the forefront, and that integrates with other Siri capabilities to enable powerful user experiences. Role responsibilities include:
-
Contribute to research, design, implementation, and evaluation of models to enhance quality and performance, and to support key functions for Personal Q&A.
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Implement and extend LLM models with fine-tuning and RLXF methodologies. Develop high quality data pipelines to support these methodologies.
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Establish target quality metrics and evaluation sets for the team to track and improve. Design novel experiments to validate/refute hypotheses and support team wide decision making.
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Collaborate with partner teams to define product requirements and priorities, and to explore opportunities for enhancements to the Personal Q&A stack.
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Develop long-term technical vision for Personal Q&A quality; identify problem areas and integrate solutions as part of a larger roadmap.
Preferred Qualifications:
In-depth knowledge and expertise in training, evaluating, and applying Deep learning models, Large Language Models for production systems
Extensive experience in building production ML systems and applications in search, recommendation systems, or information retrieval
Ability to quickly prototype ideas / solutions, and perform critical analysis
Background in: search relevance and ranking, Q&A, personalization, user behavior/response modeling, or data-driven decision-making
Advanced degree (Master's or Ph.D.) in Computer Science, Statistics, or related field, or equivalent industry work experience
Minimum Qualifications:
8+ years industry experience in Machine Learning, NLP and applying these techniques at scale
Strong software engineering skills in mainstream programming languages, such as: Python, Go, C/C++
Experience using ML frameworks (py Torch, JAX, Tensor Flow, XGBoost etc.)
Strong communication skills
Bachelors in Computer Science:
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 .
Pay & Benefits:
At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $181,100 and $318,400, and your base pay will depend on your skills, qualifications, experience, and location.
Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.
Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.
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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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