Software Engineer, Recommendation Systems

Meta is seeking a distinguished Software Engineer with deep machine learning expertise to drive transformative advances across Meta’s AI-powered products and platforms. In this role, you will operate at the intersection of foundational ML research and large-scale production systems, shaping the technical direction of machine learning infrastructure, modeling, and applied AI across the organization. You will identify and solve the hardest ML systems challenges, define architectural standards, and leverage AI-native approaches to unlock step-change improvements in how Meta builds and deploys intelligent systems at global scale.

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  12+ years of experience designing, building, and deploying large-scale machine learning systems in production environments
• Experience architecting end-to-end ML platforms spanning data pipelines, distributed training, model evaluation, and low-latency inference serving
• Experience identifying and resolving complex, cross-system ML failures including issues in model quality, training stability, feature consistency, and serving correctness
• Experience defining technical strategy and gaining organizational alignment across multiple engineering teams and cross-functional stakeholders
• Experience communicating complex ML system designs and trade-offs in writing to both technical and non-technical audiences, including executive leadership
• Experience building AI-native developer tooling or automation that measurably accelerates ML experimentation and production deployment cycles
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience applying ML to multiple product domains such as ranking and recommendation, generative AI, computer vision, or natural language understanding
• Track record of industry-recognized contributions to machine learning systems, such as publications, open-source frameworks, or widely adopted architectural patterns
• 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
• Experience with large-scale foundation model training, fine-tuning, or inference optimization across distributed hardware clusters