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AIML - Sr. Machine Learning Engineer - ML Platform Technologies (MLPT)

Apple

AIML - Sr. Machine Learning Engineer - ML Platform Technologies (MLPT)

Apple

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

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