Software Engineer, Data Infrastructure (Technical Leadership)

Meta; Menlo Park (CA); On-site; USD 250,000 - 320,000; Full time

Job summary

Meta is seeking a senior technical leader in Data Infrastructure to help shape the systems that power our data storage, processing, and analytics across Meta's products. This role will set technical direction for large-scale data platforms and drive architecture used by thousands of engineers.

The ideal candidate has deep experience building foundational data systems, guiding multi-year roadmaps, and advancing reliable, ethical AI practices in complex environments.

Qualifications

• 12+ years of experience in systems software engineering
• Experience architecting global-scale critical systems
• Experience leading multi-year technical strategy and roadmaps
• Experience identifying systemic bugs and reliability issues
• Experience influencing engineering culture across large organizations
• Demonstrated ability to integrate AI tools to optimize workflows
• Active focus on responsible AI practices

Responsibilities

• Set technical direction for large, cross-cutting data-infrastructure efforts
• Drive architecture for systems used by thousands of engineers
• Custodian of Meta's data systems supporting Frontier Models, ML, analytics, and reliability
• Lead cross-organizational alignment and execute through ambiguity
• Define engineering standards and practices adopted broadly across teams

Skills

Distributed systems

Large-scale data platforms

Cross-functional leadership

System reliability

AI safety & ethics

Performance optimization

Incident response

Prompt/context engineering

Education

Bachelor's degree in CS/CE or equivalent

Tools

Open source data infra tools

Job description

Meta is seeking a senior technical leader in Data Infrastructure to help shape the systems that power our data storage, processing, and analytics across Meta's products. Our data-infrastructure teams build and operate foundational platforms at extreme scale, world's largest Datawarehouse/Lakehouse, distributed query engines, storage and table formats, and streaming/real-time processing systems that handle exabyte-scale data with high reliability and low latency.

In this role you will set technical direction for large, cross-cutting data-infrastructure efforts, drive architecture for systems used by thousands of engineers across the org, and be the custodian of Meta's data systems in service of Frontier Models, Machine Learning Models, Analytics and Operational/Reliability Systems. We're looking for someone who has designed or led the creation of foundational data systems, query engines, storage/table formats, streaming frameworks, or a comparable large-scale data platform, and who can operate as a broad systems generalist grounded in deep data-infrastructure expertise.

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 12+ years of experience in systems software engineering, including design and implementation of large-scale distributed systems, operating systems, storage systems, or networking infrastructure
• Experience architecting and owning critical systems that operate at global scale, with demonstrated impact on reliability, performance, and engineering efficiency across multiple teams or organizations
• Experience leading multi-year technical strategy and roadmap development, including gaining cross-organizational alignment and driving execution through ambiguity
• Experience identifying and resolving systemic classes of bugs, performance regressions, or reliability issues that span multiple systems or abstraction layers
• Experience influencing technical direction and engineering culture across large organizations, including establishing standards and practices adopted broadly beyond a single team
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience rearchitecting or rebuilding foundational systems infrastructure from the ground up, including evaluating trade-offs between incremental improvement and greenfield approaches
• Track record of industry-level impact in systems engineering, such as open source contributions, published research, or widely adopted architectural patterns
• Experience driving technical and cultural change at the company level, including defining engineering excellence programs or cross-organizational reliability initiatives
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Experience applying AI and machine learning techniques to systems problems such as resource scheduling, anomaly detection, performance optimization, or automated incident response