Software Engineer, Systems ML - SW/HW Co-design at Meta
New York, New York, USA -
Full Time


Start Date

Immediate

Expiry Date

09 Oct, 25

Salary

85.1

Posted On

09 Jul, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Python, Numerics, Computer Science, Distributed Systems, Computer Engineering, Algorithm Development, Models

Industry

Computer Software/Engineering

Description

Meta is seeking an AI Software Engineer to join our Research & Development teams. The ideal candidate will have industry experience working on AI Infrastructure related topics. The position will involve taking these skills and applying them to solve for some of the most crucial & exciting problems that exist on the web. We are hiring in multiple locations.

MINIMUM QUALIFICATIONS:

  • Bachelor’s degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • Specialized experience in one or more of the following machine learning/deep learning domains: Hardware accelerators architecture, GPU architecture, machine learning compilers, or ML systems, AI infrastructure, high performance computing, performance optimizations, or Machine learning frameworks (e.g. PyTorch), numerics and SW/HW co-design
  • Experience developing AI-System infrastructure or AI algorithms in C/C++ or Python

PREFERRED QUALIFICATIONS:

  • Master/PhD degree in Computer Science, Computer Engineering
  • Technical leadership experience
  • Experience with distributed systems or on-device algorithm development
  • Experience with recommendation and ranking models
Responsibilities
  • Apply relevant AI infrastructure and hardware acceleration techniques to build & optimize our intelligent ML systems that improve Meta’s products and experiences
  • Goal setting related to project impact, AI system design, and infrastructure/developer efficiency
  • Directly or influencing partners to deliver impact through deep, thorough data-driven analysis
  • Drive large efforts across multiple teams
  • Define use cases, and develop methodology & benchmarks to evaluate different approaches
  • Apply in depth knowledge of how the ML infra interacts with the other systems around it
  • Mentor other engineers / research scientists & improve the quality of engineering work in the broader team
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