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AIML - Staff Machine Learning Engineer, Answers Knowledge and Information

Apple

AIML - Staff Machine Learning Engineer, Answers Knowledge and Information

Apple

Santa Clara, CA

·

On-site

·

Full-time

·

1mo ago

Benefits & Perks

Remote work flexibility

Flexible PTO policy

Health, dental, and vision coverage

Learning and development stipend

Required Skills

Apache Spark

Python

Airflow

About Us

Working at Apple means doing more than you ever thought possible and having more impact than you ever imagined.

Size: 10000+ employees
Industry: Technology, Information Technology, Software, Consumer Goods & Services

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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 strengthening each other's ideas. That happens because every one of us believes that we can make something wonderful and share it with the world, changing lives for the better!

In this organization, we work hard to bring the best user experiences powered by Apple Intelligence. Our team is instrumental in powering and enhancing features across a range of Apple products, including Siri, Spotlight, Safari, Messages, and more. We are an Applied ML team pushing the limits of question answering, assistant response ranking, summarization, and search technologies, while also responsible for a production service.

As part of this group, you will be doing large scale machine learning and deep learning research and development to improve Open Domain Question Answering (using both structured knowledge graph data and unstructured web data) and Summarization as well as developing fundamental building blocks needed for Artificial Intelligence. This involves developing sophisticated machine learning and large language models (LLMs) to understand user queries, retrieve and rank relevant documents across multiple sources and synthesize information across documents to provide user with a direct answer that best satisfies their intent and information seeking needs. Additionally, you will research and develop the state-of-the-art LLMs for summarizing personal data such as emails, messages, and notifications.

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You will also work with researchers and data scientists to develop, fine-tune, and evaluate domain specific Large Language Models for various tasks and applications in Apple's AI powered products and conduct applied research to transfer the cutting edge research in generative AI to production ready technologies.

Description:

In this role, you will work on LLM based question answering and Apple Intelligence features to provide concise, accurate, and grounded information to users to help them complete their tasks quickly on Apple devices.

Your core responsibilities will include:

  • Designing and developing advanced Reinforcement Learning technologies in the post-training of generative model, and delivering the end-user experience.

  • Driving cross-functional technical initiatives, collaborating with research, engineering and production teams to translate theoretical advances into deployable systems.

  • Developing novel and cutting-edge RL algorithms and improving existing ones.

  • Staying up to date with the latest RL research and integrate best practices into the team's workflow.

  • Working on the end-to-end ML lifecycle: algorithm design and implementation, data collection, model training, evaluation, and deployment.

Preferred Qualifications

Deep expertise in reinforcement learning-based post-training on LLM models, reward modeling, RLHF, RLAIF, Chain-of-thought, and agentic AI R&D.

Deep understanding of cutting edge RL algorithms and large language model.

Deep understanding in LLM pre-training, post-training.

Strong product intuition and ownership

Excellent communication skills

Minimum Qualifications

10+ years of ML experiences in search, natural language processing/understanding. Conversational AI.

Proven experience for LLM post training, including but not limited to SFT, RLHF, RLAIF, Reward Modeling, Chain-of-thought, agentic LLM.

Hands-on experience building RL pipelines and training agents in simulation or real-world environments.

Growth mindset and ability to learn new technologies

MS or Ph.D. in Computer Science, Machine Learning with a specialty in reinforcement learning, or a related field

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 .

Client-provided location(s): Santa Clara, CA

Job ID: apple-200613293-3760_rxr-660

Employment Type: OTHER

Posted: 2026-01-24T19:20:31
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Perks and Benefits

Health and Wellness

Parental Benefits

Work Flexibility

Office Life and Perks

Vacation and Time Off

Financial and Retirement

Professional Development

Diversity and Inclusion

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About Apple

Apple

Apple

Public

A 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