AI Research Scientist

1 week ago

Sunnyvale, California, United States META Full time

About the Role:
Join the Monetization team at Meta, where we are dedicated to creating innovative solutions that enhance connections between individuals and businesses. As a pivotal part of our organization, you will contribute to the development of advanced technologies that redefine social interactions and financial tools in the digital economy.

Key Responsibilities:
As a Machine Learning Research Scientist, your role will encompass:

  • Designing and implementing scalable algorithms utilizing Machine Learning and Neural Network techniques.
  • Gathering and analyzing requirements to formulate a strategic feature roadmap.
  • Leveraging your expertise in relevant research areas and programming skills for platform and framework development.
  • Optimizing Machine Learning and Neural Network algorithms for modern parallel computing environments, including distributed clusters and GPUs.
  • Collaborating with cross-functional teams to drive Meta's research product initiatives.
  • Leading and participating in groundbreaking research that results in innovative technology demonstrations and scholarly publications.

Minimum Qualifications:
To be considered for this position, candidates must possess:

  • A Bachelor's degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.
  • A PhD in Machine Learning, Artificial Intelligence, or a relevant technical discipline, or equivalent practical experience.
  • Experience in research and system development utilizing Machine Learning, Reinforcement Learning, or Deep Learning methodologies.
  • Proficiency in programming languages such as Python, C++, or Java, along with scripting languages like Perl or PHP.
  • Work authorization in the country of employment at the time of hire, with ongoing authorization throughout employment.

Preferred Qualifications:
Ideal candidates will have:

  • Demonstrated experience in research and software engineering through internships, work experience, or contributions to open-source projects.
  • Strong communication skills and experience working collaboratively in a team setting.
  • Knowledge of Data Efficient Learning, domain adaptation, and Semi-supervised Learning techniques.
  • Familiarity with architectural patterns of large-scale software applications.
  • Experience in handling and analyzing complex, high-dimensional data from diverse sources.
  • A proven ability to solve intricate problems and evaluate various solutions and perspectives.
  • A track record of significant achievements, evidenced by grants, fellowships, patents, or first-authored publications in prestigious conferences.

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