Senior Staff Machine Learning Engineer

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.

Responsibilities

Identify and solve the most complex machine learning systems problems across the organization, including model training efficiency, large-scale inference optimisation, and cross-system ML pipeline reliability
Define and own the technical architecture for critical ML infrastructure components, ensuring designs are extensible, maintainable, and capable of supporting the next generation of model development at Meta's scale
Establish engineering standards and invariants for ML systems that prevent entire categories of reliability, correctness, and performance issues across multiple teams
Drive cross-organizational technical strategy by assessing emerging ML technologies, evaluating trade-offs between competing approaches, and influencing roadmap decisions with leadership and cross-functional partners
Lead the design and delivery of org-wide ML platform initiatives, coordinating across engineering teams, research, product, and data science to align on priorities and drive execution through ambiguity
Develop new metrics and data-driven frameworks for evaluating ML system health, model quality, and infrastructure efficiency, and connecting them to organization-level goals
Mentor and coach engineers across the organization on ML system design, debugging complex models and infrastructure failures, and developing scalable solutions to recurring technical challenges
Collaborate with privacy, security, and compliance teams to design ML systems that uphold user safety, meet regulatory requirements, and set the standard for responsible AI practices within the business domain
Identify and eliminate engineering bottlenecks through improved tooling, automation, and process improvements that make ML development faster and more reliable across the organization
Contribute to recruiting efforts by building relationships with candidates who bring critical ML systems expertise, and serve as a technical reference point for evaluating engineering talent at the most senior levels

Qualifications

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)