Software Engineer, Systems ML

Meta

$120K — $160K *
Information Technology
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • Bachelor's degree in Computer Science or related technical field, or equivalent experience
  • 5-7 years of specialized experience in machine learning and AI infrastructure
  • Proficiency in C/C++ or Python for AI-System development
  • Experience with performance optimizations and high-performance computing
  • Familiarity with AI frameworks such as PyTorch
  • Technical leadership and mentorship experience
  • Demonstrated ability to integrate AI tools for workflow optimization

Responsibilities

  • Apply AI infrastructure and hardware acceleration techniques to optimize ML systems
  • Set project goals related to AI system design and developer efficiency
  • Conduct data-driven analysis to influence partners and deliver impacts
  • Coordinate large-scale efforts across multiple teams
  • Define use cases and develop methodologies to evaluate different approaches
  • Leverage knowledge of ML infrastructure in systems interactions
  • Mentor engineers and researchers to improve engineering quality

Benefits

  • Opportunity to work on cutting-edge AI technologies
  • Collaborative and innovative work environment
  • Access to professional development and training resources
  • Flexible work location options in multiple regions
  • Strong focus on ethical AI practices and responsible development
Full Job 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.

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

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
• 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

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