Senior Staff Machine Learning Engineer

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

Minimum 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

Preferred Qualifications

• 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)

About Meta

Equal Opportunity

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