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Machine Learning Engineer - MLR

Apple

Machine Learning Engineer - MLR

Apple

Cupertino, CA

·

On-site

·

Full-time

·

1mo ago

Benefits & Perks

Learning and development stipend

Top Tier compensation with equity

Health, dental, and vision coverage

Flexible PTO policy

Annual team offsites

Required Skills

Airflow

Python

TensorFlow

About the Role

Play a part in building the next revolution of machine learning technology. We're looking for passionate mid-level and junior researchers to work on ambitious curiosity driven long-term research projects that will impact the future of Apple, and our products. In this role, you'll have the opportunity to work on innovative foundational research, with focus on Multimodal LLMs and AI Agents. As a member of the team, you will be inspired by a diversity of challenging problems, collaborate with world-class machine learning engineers and researchers to impact the future of Apple products, and publish some of your results in high-quality scientific venues.

Description

You will propose and co-develop innovative research in the areas of Multimodal LLMs and AI Agents, execute it through implementation and experimentation in collaboration with other researchers and engineers. The research questions revolve around modeling and data decisions that enable strong reasoning and planning capabilities in Multimodal LLMs in particular and Foundation Models in general; techniques and methods of enabling interactive and embodied applications of such models towards AI Agents. Work will involve hands-on rapid prototyping of ideas and use of scalable distributed compute. You will work closely with both researchers but also potentially product partners, resulting in publications as well as prototypes for internal product efforts.

Minimum Qualifications

  • PhD, MS or equivalent in Computer Science, Engineering, or equivalent; strong mathematical skills in linear algebra and statistics
  • Demonstrated expertise in Machine Learning or Computer Vision
  • Publication record in relevant conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, CoRL, etc)
  • Hands-on experience working with deep learning toolkits such as Jax or Py Torch
  • Strong passion for systems-based and mission-driven research with focus on execution and velocity
  • Ability to formulate a research problem, design, experiment, implement and communicate solutions
  • Ability to work in a diverse collaborative environment as part of larger projects

Preferred Qualifications

  • Expertise in Foundational Models and/or Reinforcement Learning
  • Experience with Scalable ML Systems and Frameworks

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