Sr. Staff Software Engineer, Systems Infrastructure

This role will be based in Mountain View, CA.

At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team.

As part of our world-class software engineering team, you will take the lead in building the next-generation infrastructure and platforms for LinkedIn, including but not limited to: service delivery platform, scalable data storage infrastructure, graph infrastructure, analytics platform, streams processing and data pipelines, cutting-edge search platform, best-in-class AI/ML infrastructure, Kubernetes compute infrastructure, media infrastructure, etc. You will work and learn among the best, putting to use your passion for distributed technologies and algorithms, API design and systems design, and your passion for writing code that performs at massive scale. LinkedIn has pioneered many well-known open-source infrastructure projects including Apache Kafka, Pinot, Azkaban, Samza, Venice, Datahub, Feather, etc. We also work with industry standard open source infrastructure technologies like Kubernetes, GRPC and GraphQL - come join our infrastructure teams and share the knowledge with a broader community while making a real impact within our company.

The Metrics Platform team's mission is to provide a highly available and scalable metrics platform that powers the world-class observability solutions needed to support LinkedIn's needs and growth. We build and operate the foundational telemetry systems that every service, engineer, and AI-first product at LinkedIn relies on to understand the health and performance of their systems in near real time.

The platform is a dial-tone-grade, geo-redundant Ring-0 dependency. When it is degraded, the company is effectively flying blind, and critical alerts may not fire. Our customers are, quite literally, every engineering team at LinkedIn.

As a Senior Staff Engineer on the Metrics Platform team, you will set the technical direction for systems operating at company scale:

• Ingest billions of metric events per minute from millions of containers and service instances across LinkedIn's global fleet, enabling rapid ingestion and querying of metric data.

• Store and serve highly multidimensional time-series data with approximately 12 months of retention, preserving historically accurate views while maintaining interactive query latencies.

• Guarantee dial-tone-grade, geo-redundant availability to ensure critical alerts fire on time for every LinkedIn product and infrastructure team.

• Drive cost efficiency and multi-tenancy at scale through write-time aggregations and quota management to protect against metric fan-out.

• Shape LinkedIn's observability for the AI-first future as we enable more agentic flows throughout LinkedIn's engineering systems.

You will lead the strategy, design, implementation, and operation of the Metrics Platform, with direct technical influence across the Observability Engineering organization and partner teams. You will serve as a go-to technical expert for LinkedIn's metrics systems.

What You'll Do

• Own the technical strategy and multi-year roadmap for major components of the Metrics Platform, developing forward-looking solutions to large, open-ended problems that extend beyond the immediate team.

• Design, implement, and optimize large-scale distributed systems across the metrics pipeline, including high-throughput ingestion, multidimensional time-series storage, write-time aggregation, and low-latency querying, with reliability, security, privacy (no PII/EUII in metrics), and cost efficiency built in.

• Solve the most challenging problems in the domain, including metric cardinality and fan-out management, accurate pre-aggregated percentiles, geo-redundant high availability for a Tier-0 service, query performance over large datasets, and efficient long-term retention.

• Improve observability, self-service, and developer productivity for platform customers by enabling querying, dashboarding, and alerting experiences that reduce time to detection and resolution.

• Set the technical bar by establishing engineering best practices, design patterns, and quality standards; leading design reviews; and mentoring Staff and senior engineers to strengthen the technical culture of a fast-moving team (Leadership / Execution / Craft).

• Partner and communicate effectively across the Observability Engineering organization and partner teams, translating business and reliability requirements into robust architectures and clear execution plans spanning multiple quarters and years.

Department: Engineering
Experience Level: Mid-Senior Level