Software Engineer, Systems ML

About the Role

Meta is seeking an AI Software Engineer to join our Research & Development teams. The position will involve applying AI infrastructure and hardware acceleration techniques to solve crucial and exciting problems on the web. We are hiring in multiple locations.

Key Responsibilities

• Apply relevant AI infrastructure and hardware acceleration techniques to build & optimize 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, 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 ML infra interacts with other systems.
• Mentor other engineers/research scientists & improve the quality of engineering work in the broader team.

Requirements & Tech Stack

• 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
• ML systems
• AI infrastructure
• High performance computing
• Performance optimizations
• Machine learning frameworks (e.g., PyTorch)
• Numerics
• 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.
• 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.