Sr. Staff Software Engineer, Systems Infrastructure

LinkedIn•Mountain View, CA

1d•Hybrid

About The Position

LinkedIn is hiring a Senior Staff Engineer for its Metrics Platform team, based in Mountain View, California. The role is hybrid, with work performed both from home and from a LinkedIn office on select days according to team business needs. The Metrics Platform provides highly available, scalable telemetry infrastructure for LinkedIn’s engineering teams and products. It is a geo-redundant, Tier-0 dependency that supports near-real-time monitoring and critical alerting. The platform ingests billions of metric events per minute from millions of containers and service instances, stores and serves multidimensional time-series data with approximately 12 months of retention, and supports interactive queries while preserving historical accuracy. The Senior Staff Engineer will lead the strategy, design, implementation, and operation of major parts of the platform, setting technical direction and influencing the Observability Engineering organization and partner teams. The role focuses on reliability, security, privacy, scalability, query performance, retention, cost efficiency, and multi-tenancy, while helping shape observability systems for AI-first engineering workflows.

Requirements

BA/BS degree in Computer Science or a related technical discipline, or equivalent practical experience.
At least 5 years of industry experience in software design, development, and algorithm-related solutions.
At least 5 years of programming experience in object-oriented languages such as Java, C++, Python, Go, or C#, and/or functional languages such as Scala.
Hands-on experience building and operating distributed systems, large-scale systems, databases, and/or backend APIs.

Nice To Haves

BS and 10+ years, MS and 9+ years, or PhD and 6+ years of relevant work experience.
Experience with time-series or metrics systems, observability or telemetry platforms, high-throughput streaming pipelines, or OLAP/analytics storage and query engines.
Experience with Kafka, OpenTelemetry, Grafana, Avro, Kubernetes, gRPC, and large-scale time-series/metrics storage and query engines.
Experience operating highly reliable, geo-redundant services under strict reliability, latency, and cost constraints.
Experience managing high-cardinality multidimensional data, pre-aggregation, retention tiering, and query optimization at scale.
Demonstrated ability to drive cross-team and cross-organizational technical initiatives and influence foundational systems.
Experience mentoring Staff and senior engineers and establishing a strong technical culture.
Experience applying AI/ML to operational problems such as anomaly detection, forecasting, and automated root-cause analysis.

Responsibilities

Own the technical strategy and multi-year roadmap for major components of the Metrics Platform, developing forward-looking solutions to large, open-ended problems.
Design, implement, and optimize distributed systems across the metrics pipeline, including high-throughput ingestion, multidimensional time-series storage, write-time aggregation, and low-latency querying.
Build reliability, security, privacy—including avoiding PII/EUII in metrics—and cost efficiency into platform systems.
Address metric cardinality and fan-out management, accurate pre-aggregated percentiles, geo-redundant availability, large-dataset query performance, and efficient long-term retention.
Improve observability, self-service, and developer productivity through querying, dashboarding, and alerting experiences that reduce time to detection and resolution.
Establish engineering best practices, design patterns, and quality standards; lead design reviews; and mentor Staff and senior engineers.
Partner across Observability Engineering and other teams, translating business and reliability needs into robust architectures and clear execution plans spanning multiple quarters and years.
Shape the platform to support AI-first and agentic workflows across LinkedIn engineering systems.