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Machine Learning FEA Engineer

Apple

Machine Learning FEA Engineer

Apple

Culver City, CA

·

On-site

·

Full-time

·

1mo ago

Benefits & Perks

Annual team offsites

Flexible PTO policy

Health, dental, and vision coverage

Top Tier compensation with equity

Wellness benefits

Required Skills

Python

TensorFlow

Airflow

About the Role

Imagine what you can do here! We are committed to pushing the boundaries of innovation and engineering excellence in product designs through machine learning and FEA simulations. We truly believe in the power of predictive simulation to make the impossible possible, transform industries and improve people's lives. As a member of the Product Design FEA team, you will play a pivotal role in developing innovative machine learning technologies and directly impact the success of new i Phone, i Pad, Mac, Apple Watch, Vision Pro and many more future products. Come join us and put a dent in the universe!

Description

As a core member of the product design team, you will be responsible for developing and implementing ground-breaking machine learning methods that are based on predictive finite element simulations and important design load cases. The machine learning models will drive rapid design iterations by assessing potential risks and optimizing design trade-offs. You will be fully integrated with the product design team from the earliest stages to engineer ground breaking products.

Preferred Qualifications

  • Strong expertise in GNNs, CNNs, and transformer-based architectures
  • Implement and optimize these models for large-scale datasets on scalable ML platforms
  • Ability to work independently in white space and deal with an incredible fast-paced environment
  • Excellent cross-functional collaboration and written and verbal communication skills
  • Ph.D. in Computer Science, Machine Learning, Mechanical Engineering, or a similar discipline
  • Publications in top journals or conferences

Minimum Qualifications

  • Strong Expertise in Machine Learning, Deep Learning, and Optimization
  • Knowledges of Finite Element Analysis and/or other numerical methods in computational physics and mechanics
  • Proficiency in Python and relevant packages for ML
  • Outstanding communication skills
  • Passion for creating innovative, high-quality products
  • Desire to work in a fast-paced environment with passion for creating cutting edge products
  • M.S. in Computer Science, Machine Learning, Mechanical Engineering, or a similar discipline along with 3+ years of relevant experience

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