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Machine Learning Audio R & D Engineer

Apple

Machine Learning Audio R & D Engineer

Apple

Cambridge, MA

·

On-site

·

Full-time

·

1mo ago

Benefits & Perks

Top Tier compensation with equity

Health, dental, and vision coverage

Learning and development stipend

Remote work flexibility

Annual team offsites

Required Skills

Python

TensorFlow

Apache Spark

About the Role

The AMT Audio team is looking for a highly motivated and talented research and development machine learning engineer to develop models for speech and audio applications.

The Audio and Media Technologies (AMT) is at the center of Apple's innovative products, including the Mac, i Phone, i Pad, Apple Watch, Apple TV, macOS, iOS, watchOS and tvOS. AMT's Core Audio team provides audio foundation for various high profile features like Siri, phone calls, Face Time, media capture, playback, and API's for third party developers to enrich our platforms. The team is looking for talented engineers who are passionate about building audio software products for millions of customers and care about overall user experience. You will be revolutionizing and contributing to future audio technologies.

Responsibilities

As part of the audio team, you will develop machine learning models for speech enhancement, augmented hearing and hearing health applications. Train, fine-tune and compress models for production. Contribute to the team's workflows building pipelines, writing datasets, and creating evaluation frameworks. Participate in cross-functional teams developing features and shipping products. Create intellectual property in the form of patents and publications.

Preferred Qualifications

  • Experience developing models and algorithms for speech enhancement, source separation and assistive hearing
  • Demonstrated innovation in machine learning for audio (patents, publications)
  • Fundamentals of audio and speech signal processing
  • PhD, in CS or EE, Industry and research experience

Minimum Qualifications

  • 4+ years experience in machine learning development, including audio and speech processing applications.
  • Practical and theoretical knowledge of machine learning architecture design and training/testing methodologies.
  • Experience building machine learning pipelines
  • Experience optimizing and compressing machine learning models for resource constrained platforms.
  • Excellent software architecture and programming skills in Python, C, C++
  • Knowledge of audio technologies
  • MS, in CS or EE, industry and research 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