Senior Staff Machine Learning Engineer in London

Meta is reimagining shopping on Facebook, Instagram, and Muse. The vision is to make every piece of media shoppable. Behind that vision is our Product Recognition and Intelligence stack. It is the tech that looks at any photo, video, and text, and knows exactly which products are in it and how they relate to other products. We are building it to work at Meta's scale. Over 3 billion distinct users every day. Billions of items power a multibillion-dollar ad business.

We are hiring a senior staff Machine Learning Engineer to lead this journey. You will tackle the hardest ML systems challenges in training, serving, and evaluation. You will set the architecture and standards that raise the bar across organizations. You will ship state-of-the-art models to production where they touch billions.

If you want to have an impact at a global scale and love building tech that actually ships, come build with us.

• 12+ years of experience designing, building, and operating large-scale machine learning systems in production environments
• Experience architecting ML infrastructure components such as distributed training pipelines, model serving systems, feature stores, or ML evaluation frameworks at a significant scale
• Experience in identifying and resolving systemic performance, reliability, or correctness issues that span multiple ML systems or abstraction layers
• Experience influencing technical direction across multiple engineering teams through written proposals, design reviews, and cross-functional stakeholder alignment
• Experience defining engineering standards, architectural patterns, or platform capabilities that have been adopted broadly across an organization
• Experience in building large-scale object recognition models
• Experience collaborating with applied research teams to translate theoretical ML advances into production-grade system improvements
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience developing novel approaches to ML observability, experiment management, or model lifecycle tooling that improved engineering velocity across teams
• Experience leading multi-year ML platform or infrastructure migrations in complex, mature technical environments
• Experience in training large language models to production systems at scale
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

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