Software Engineer, Systems ML

Meta Meta · Big Tech · Menlo Park, CA +2

Software Engineer focused on AI infrastructure and hardware acceleration for ML systems, optimizing performance and efficiency for Meta's products. Involves C/C++/Python development, distributed systems, and potentially on-device algorithms. Requires technical leadership and experience with ML frameworks and hardware architectures.

What you'd actually do

  1. Apply relevant AI infrastructure and hardware acceleration techniques to build & optimize our intelligent ML systems that improve Meta’s products and experiences
  2. Goal setting related to project impact, AI system design, and infrastructure/developer efficiency
  3. Directly or influencing partners to deliver impact through deep, thorough data-driven analysis
  4. Drive large efforts across multiple teams
  5. Define use cases, and develop methodology & benchmarks to evaluate different approaches

Skills

Required

  • 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
  • Experience with distributed systems or on-device algorithm development
  • Experience with recommendation and ranking models
  • Technical leadership experience
  • A Bachelor's degree in Computer Science, Computer Engineering, relevant technical field and 7+ years of experience in AI framework development or accelerating deep learning models on hardware architectures OR a Master's degree in Computer Science, Computer Engineering, relevant technical field and 4+ years of experience in AI framework development or accelerating deep learning models on hardware architectures OR a PhD in Computer Science Computer Engineering, or relevant technical field and 3+ years of experience in AI framework development or accelerating deep learning models on hardware architectures.
  • Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
  • Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

Nice to have

  • AI framework development
  • accelerating deep learning models on hardware architectures
  • prompt/context engineering
  • agent orchestration

What the JD emphasized

  • AI Infrastructure
  • hardware acceleration
  • ML systems
  • performance optimizations
  • distributed systems
  • on-device algorithm development
  • AI framework development
  • accelerating deep learning models on hardware architectures

Other signals

  • AI infrastructure
  • hardware acceleration
  • ML systems
  • performance optimizations
  • distributed systems
  • on-device algorithm development