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Research Engineer, Monetization AI

Meta

Research Engineer, Monetization AI

Meta

Sunnyvale, CA

·

On-site

·

Full-time

·

2w ago

Compensation

$183,040 - $183,040

Benefits & Perks

Equity

Bonus

Equity

Required Skills

Python

PyTorch

Machine Learning

Deep Learning

Recommender Systems

We are the Monetization Ranking and Foundational AI organization, dedicated to delivering personalized ads that maximize both user utility and advertiser value. We focus on advancing AI, ML and Rec Sys technologies for all aspects of Monetization, including ranking, retrieval, model architecture, and optimization. By consistently integrating cutting-edge AI/ML/Rec Sys advancements, we help Meta's products achieve long-term goals and have contributed tens of billions in revenue. With our growing impact, we're seeking AI/ML/Rec Sys specialists to join our team and drive SOTA research and production across the Monetization organization.

Research Engineer, Monetization AI Responsibilities:

  • Develop and implement large-scale model architectures, leveraging model scaling and transfer learning techniques
  • Prioritize training scalability and signal scaling to optimize model performance, efficiency, and reliability
  • Develop and apply Next Gen sequence learning techniques to drive advancements in recommender systems and machine learning
  • Design and implement generative modeling solutions for data augmentation
  • Develop and deploy machine learning pipelines
  • Collaborate with cross-functional teams to design and optimize ML systems, leveraging expertise in hardware-software co-design, including quantization, compression, and resource-efficient AI, to drive performance improvements and efficiency gains
  • Develop and implement innovative solutions for data-related challenges, utilizing knowledge of semi/self-supervised learning, generative techniques, sampling, debiasing, domain adaptation, continual learning, data augmentation, cold-start, content understanding, and large language models

Minimum Qualifications:

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • Research experience in machine learning, deep learning, natural language processing, and/or recommender systems
  • Experience with developing machine learning models at scale from inception to business impact
  • Programming experience in Python and hands-on experience with frameworks such as Py Torch
  • Exposure to architectural patterns of large scale software applications

Preferred Qualifications:

  • PhD in AI, Computer Science, Data Science, or related technical fields
  • Master's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • First author publications at peer-reviewed AI conferences (e.g., NeurIPS, ICML, ICLR, ICCV, CVPR, ACL, EMNLP, Rec Sys, KDD, WSDM, The Web Conf, ICDM, AAAI)
  • Direct experience in generative AI, LLMs, Rec Sys, ML research

About Meta:

Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and Whats App further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today-beyond the constraints of screens, the limits of distance, and even the rules of physics.

Meta is proud to be an Equal Employment Opportunity and Affirmative Action employer. We do not discriminate based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law. Meta participates in the E-Verify program in certain locations, as required by law. Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment.

Meta is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at accommodations-ext@meta.com.

$88.46/hour to $257,000/year + bonus + equity + benefits

Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.

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About Meta

Meta

Meta

Public

A social technology company that enables people to connect, find communities, and grow businesses.

10,001+

Employees

Menlo Park

Headquarters

$800B

Valuation

Reviews

3.4

26 reviews

Work Life Balance

2.3

Compensation

4.2

Culture

2.8

Career

3.1

Management

2.1

45%

Recommend to a Friend

Pros

Excellent compensation and benefits

Smart and talented colleagues

Fast-paced and challenging work environment

Cons

Frequent layoffs and job insecurity

Poor leadership and management accountability

High stress and competitive work environment

Salary Ranges

40,175 data points

Mid/L4

Mid/L4 · Data Scientist

3,113 reports

$284,667

total / year

Base

$179,458

Stock

$79,981

Bonus

$25,228

$193,897

$434,902

Interview Experience

6 interviews

Difficulty

4.2

/ 5

Duration

21-35 weeks

Offer Rate

17%

Experience

Positive 17%

Neutral 17%

Negative 66%

Interview Process

1

Application Review

2

Recruiter Screen

3

Online Assessment

4

Technical Phone Screen

5

Coding Interviews

6

System Design Interview

7

Behavioral Interview

8

Final Loop/Hiring Manager Round

Common Questions

Coding/Algorithm

System Design

Behavioral/STAR

Technical Knowledge

Live Coding