Owns design, architecture, and operation of database and storage systems for high-throughput, low-latency AI platform workloads. Requires 8+ years experience with transactional databases like Spanner, Postgres; expertise in data modeling, distributed systems, and production reliability.
Salary not listed
On-site8+ YOEDevOps / SRE
About the role
Responsibilities
Own the design and evolution of the transactional systems that power the platform
Define architecture for database and storage systems under high-throughput, low-latency workloads
Make and drive decisions around data modeling, indexing, replication, and consistency
Debug and resolve complex production issues, including contention, replication behavior, and performance bottlenecks
Establish standards for reliability, observability, and operability across the platform
Partner with product and research teams to support evolving and often ambiguous requirements
Drive improvements in performance, scalability, and cost across the system
Mentor engineers and raise the bar for system design and technical decision-making
Requirements
8+ years of experience building and operating production systems at scale, with ownership of critical infrastructure
Strong experience designing and operating transactional systems and databases (Spanner, Postgres, MySQL, or similar)
Deep understanding of data modeling, indexing, transactions, concurrency, and consistency tradeoffs
Experience owning systems with strict reliability and performance requirements in production
Strong experience debugging complex production issues and reasoning about failure modes
Experience designing distributed systems or large-scale infrastructure where tradeoffs are non-trivial
Proven ability to define architecture and drive technical decisions end-to-end
Strong judgment in balancing performance, reliability, and cost
Ability to operate effectively in ambiguous, fast-moving environments with high ownership
Preferred
Experience with database internals, storage systems, or query engines
Experience building infrastructure for AI/ML systems or data platforms
Experience in early-stage or high-growth environments
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