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Senior Software Engineer - Generative AI & ML, Customer Systems

Apple

Senior Software Engineer - Generative AI & ML, Customer Systems

Apple

Austin, TX

·

On-site

·

Full-time

·

5d ago

At Apple, we are driven to deliver exceptional experiences through ultra-fast, thoughtfully designed, and meticulously crafted solutions. Our team is not just any group; we are a highly motivated, fast-paced, and dynamic collective of professionals committed to scaling new heights and achieving excellence. We seek individuals who strive beyond mediocrity and are relentless in their pursuit of perfection.
Contribute to model development and fine-tuning workflows for generative AI features.

Design and evaluate retrieval strategies for grounding large models in product-relevant data.

Prototype and benchmark multi-agent collaboration systems for structured reasoning tasks.

Partner with data and platform engineers to ensure scalable deployment and monitoring.

Description:

The Customer Support AI team is responsible for building multi-turn, conversational, agentic applications and frameworks to support Apple customers across numerous lines of business. You'll be contributing hands on to a team that consists of engineers, data scientists & researchers to enhance a multi-modal, multi-agent platform with a key focus on incorporating research to improve, latency, cost and customer experience. This is an incredible opportunity to contribute innovation & research to a well established generative AI platform within Apple. There is a huge amount of opportunity and growth within this space!

Preferred Qualifications:

Exposure to multi-agent orchestration frameworks in Rust and Python.

Familiarity with modern deep learning frameworks such as Py Torch, Tensor Flow, or JAX.

Experience with data preprocessing, tokenization, and pipeline automation.

Proficiency in machine learning libraries (transformers, datasets).

Strong problem-solving and collaboration skills, with the ability to learn quickly and adapt to production-grade systems.

Experience working with Multi-modal LLMs to enable Voice capabilities is a plus or prior experience with STT, TTS systems.

Experience with deploying to cloud environments (AWS, GCP, on-remote hybrid) is required.

Bachelor's or Master's degree in Computer Science, Machine Learning, or related field, or equivalent practical experience.

Minimum Qualifications:

5+ years of hands-on experience in ML, backend engineering, data engineering

1-2 years of hands-on experience in training, fine-tuning, or evaluating LLMs

Foundational understanding of RAG architectures and vector-based retrieval systems

Strong experience partnering with business and engineering team to deliver AI solutions

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

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

L2

L3

L4

L5

L6

L2 · Business Analyst L2

0 reports

$114,215

total / year

Base

$45,686

Stock

$57,108

Bonus

$11,422

$79,951

$148,480

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