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Research Scientist Intern, PyTorch Distributed (PhD)

Meta

Research Scientist Intern, PyTorch Distributed (PhD)

Meta

Menlo Park, CA

·

On-site

·

Internship

·

2w ago

Compensation

$91,800 - $91,800

Benefits & Perks

E Verify

Required Skills

Machine Learning

Deep Learning

Distributed Systems

Meta is seeking a Research Scientist Intern to join our Meta Py Torch Distributed Team. Our team's mission is to make Py Torch faster and easier to use in order to create and maintain a state-of-the-art machine learning framework that is used across Meta and the entire industry. The key challenges in the team are composing multiple distributed training features to support growing model complexity, jointly optimizing computation and communication to maximize hardware utilization, and automating parallelizations to boost usability.

Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

Research Scientist Intern, Py Torch Distributed (PhD) Responsibilities:

  • Apply relevant AI and machine learning techniques to advance the state-of-the-art in machine learning frameworks.
  • Collaborate with users of Py Torch to enable new use cases for the framework both inside and outside Meta.
  • Develop novel, accurate AI algorithms and advanced systems for large scale distributed training and inference.
  • Leverage graph-based and compiler-based technologies to optimize distributed training and distributed inference use-cases

Minimum Qualifications:

  • Currently has, or is in the process of obtaining, PhD degree in the field of Computer Science or a related STEM field
  • Experience in one or more of the following machine learning/deep learning domains: Large scale training and inference ML Systems Research, ML theory: Basic knowledge about ML models in different modalities like LLM (Large Language Models), Vision (VITS, MVITS) and Multimodal and how scale impacts performance, ML systems: AI infrastructure, machine learning accelerators, high performance computing, machine learning compilers, GPU architecture, machine learning frameworks, distributed systems, on-device optimization
  • Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

Preferred Qualifications:

  • Experience or knowledge on training models at scale using Py Torch/Tensor Flow/JAX
  • Experience or knowledge on working with a distributed GPU cluster
  • Intent to return to degree program after the completion of the internship/co-op
  • Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, MLSys, ASPLOS, PLDI, CGO, PACT, ICML, or similar
  • Experience working and communicating cross functionally in a team environment

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.

$7,650/month to $12,134/month + 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