Key skills
AIMachine LearningMySQLLeadership
About this role
Meta is seeking a distinguished Software Engineer specializing in database internals to drive the technical direction of our MySQL infrastructure that powers Meta's family of products and platforms. In this role, you will architect and evolve critical database systems within our MySQL team — including our custom storage layer (MyRocks) and replication layer (MyRAFT) — that underpin billions of user interactions daily. You will identify and solve the hardest database-level challenges across the organization, define multi-year technical strategy to adapt our relational database systems to support Meta's AI needs, and serve as a force multiplier for engineering teams through deep database expertise, AI-native workflows, and cross-functional leadership.
Responsibilities
Define and drive the long-term technical vision and architecture for Meta's MySQL infrastructure, including the MyRocks storage engine and MyRAFT replication layer
* Architect database system enhancements to support Meta's evolving AI workloads and large-scale data requirements
* Identify and resolve the most complex database-level performance, reliability, and correctness challenges that span storage, replication, query execution, and transaction processing
* Lead the design and implementation of critical database internals — including storage engine optimizations, replication protocols, query planning, and transaction management — ensuring correctness, efficiency, and long-term maintainability
* Establish extensible technical foundations, coding standards, and architectural patterns that improve consistency and velocity across database engineering teams
* Develop and operationalize testing frameworks, verification methodologies, and data integrity checks that prevent database bugs and reliability regressions at scale
* Leverage AI tooling and automation to optimize database performance, accelerate development workflows, and identify optimization opportunities
* Partner with product, infrastructure, and platform engineering teams to translate complex database requirements into durable technical designs, influencing roadmaps across organizational boundaries
* Define and track database-level metrics, SLOs, and performance guardrails that connect engineering outcomes to organization-level priorities
* Mentor engineers across the organization on database design principles, query optimization techniques, and storage engine internals
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
* 12+ years of experience in database systems engineering, including design and implementation of database internals such as storage engines, replication systems, query optimizers, or transaction processing
* Experience working on database teams in the industry, with deep expertise in database architecture and internals
* Experience architecting and owning large-scale database infrastructure used across multiple teams or organizations, including driving multi-year technical roadmaps
* Experience identifying and resolving complex database performance, reliability, or correctness issues spanning storage, replication, or query execution layers
* Experience defining engineering standards, architectural patterns, and verification methodologies that improve database system quality and consistency
* Experience communicating complex database architecture and technical strategy in writing and presentations to both technical and non-technical stakeholders Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
* Experience leading database migrations, schema evolution, or platform modernization efforts in large-scale production environments
* Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
* Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
* Experience with database internals, like storage engines
* Experience with distributed database systems, consensus protocols (such as Raft or Paxos), or building highly available data infrastructure
* Experience applying AI and machine learning techniques to database optimization problems, such as query optimization, workload prediction, or automated performance tuning
Responsibilities
Define and drive the long-term technical vision and architecture for Meta's MySQL infrastructure, including the MyRocks storage engine and MyRAFT replication layer
* Architect database system enhancements to support Meta's evolving AI workloads and large-scale data requirements
* Identify and resolve the most complex database-level performance, reliability, and correctness challenges that span storage, replication, query execution, and transaction processing
* Lead the design and implementation of critical database internals — including storage engine optimizations, replication protocols, query planning, and transaction management — ensuring correctness, efficiency, and long-term maintainability
* Establish extensible technical foundations, coding standards, and architectural patterns that improve consistency and velocity across database engineering teams
* Develop and operationalize testing frameworks, verification methodologies, and data integrity checks that prevent database bugs and reliability regressions at scale
* Leverage AI tooling and automation to optimize database performance, accelerate development workflows, and identify optimization opportunities
* Partner with product, infrastructure, and platform engineering teams to translate complex database requirements into durable technical designs, influencing roadmaps across organizational boundaries
* Define and track database-level metrics, SLOs, and performance guardrails that connect engineering outcomes to organization-level priorities
* Mentor engineers across the organization on database design principles, query optimization techniques, and storage engine internals
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
* 12+ years of experience in database systems engineering, including design and implementation of database internals such as storage engines, replication systems, query optimizers, or transaction processing
* Experience working on database teams in the industry, with deep expertise in database architecture and internals
* Experience architecting and owning large-scale database infrastructure used across multiple teams or organizations, including driving multi-year technical roadmaps
* Experience identifying and resolving complex database performance, reliability, or correctness issues spanning storage, replication, or query execution layers
* Experience defining engineering standards, architectural patterns, and verification methodologies that improve database system quality and consistency
* Experience communicating complex database architecture and technical strategy in writing and presentations to both technical and non-technical stakeholders Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
* Experience leading database migrations, schema evolution, or platform modernization efforts in large-scale production environments
* Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
* Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
* Experience with database internals, like storage engines
* Experience with distributed database systems, consensus protocols (such as Raft or Paxos), or building highly available data infrastructure
* Experience applying AI and machine learning techniques to database optimization problems, such as query optimization, workload prediction, or automated performance tuning