Abstract
How do you deliver relevant and personalized recommendations to nearly a billion professionals—instantly, reliably, and at scale? At LinkedIn, the answer has been a multi-year journey of architectural reinvention. What started as an offline, batch-oriented system for connection suggestions has evolved into a real-time, cloud-hosted platform powering mission-critical recommendations across multiple surfaces—including "People You May Know", "Follows", Jobs, and Video.
This talk will unpack that architectural migration across four key phases: from offline scoring (with massive precomputation and high storage waste), to nearline scoring (for reactive freshness), to online scoring (with real-time inference and candidate flexibility), and finally to remote scoring (with GPU-accelerated inference in the cloud). Each phase brought its own trade-offs in latency, freshness, cost, and scale—and each pushed us closer to a system capable of delivering intent-aware, personalized recommendations on-demand.
We’ll dive deep into the key innovations that made this evolution possible: the decoupling of candidate generation from scoring pipelines, the adoption of Embedding-Based Retrieval (EBR) for addressing the cold-start problem, and the integration of LLM-powered ranking models for nuanced personalization. These changes not only enabled 90%+ reductions in offline compute/storage costs but also unlocked major gains in member engagement and system adaptability.
Beyond PYMK and Follows, we’ll also show how these architectural patterns are being applied to next-generation systems like LinkedIn Jobs (with career-intent sensitivity) and Video (where content freshness and rapid feedback loops dominate). By comparing the stacks, we’ll highlight how a unified architectural foundation enables tailored optimization across product surfaces.
Whether you’re modernizing legacy batch systems or architecting for real-time recommendations from day one, this session will equip you with practical lessons in relevance design, infra trade-offs, and relevance-first thinking—at the scale of billions.
Key Takeaways:
- Design for Cold-Start Like It's Day One
- Architectural Shifts Create Non-Linear Gains
- Retrieval Isn’t a Model Problem—It’s a Portfolio Strategy
- Decouple to Win: Split Generation From Scoring
- Staleness Kills—Freshness Converts
Speaker
Nishant Lakshmikanth
Engineering Manager @LinkedIn, Leading Infrastructure for "People You May Know" and "People Follows", Previously @AWS and @Cisco
Nishant is an Engineering Manager at LinkedIn, where he leads the infrastructure for People You May Know (PYMK) and People Follows (PF)—two foundational recommendation systems responsible for building a multi-billion dollar revenue stream annually and creating an engaging and meaningful ecosystem at scale. Over the past several years, he has been instrumental in scaling the underlying relevance infrastructure by migrating multiple pipelines from offline and nearline to online, inventing powerful candidate sourcing mechanisms, and reimagining the retrieval landscape to drive sustained business impact and unlock new ways of solving cold-start problems.
At LinkedIn, Nishant also built the control plane for all data systems from the ground up, enabling seamless orchestration, quota automation, and real-time capacity management across LinkedIn’s critical services. He led the development of the Multi-Tenancy-As-A-Service (MAAS) framework, which now underpins key infrastructure shared across teams. His recent efforts focus on integrating large language models (LLMs) into production pipelines, enhancing entity-based retrieval, and optimizing model serving for low-latency, high-throughput use cases.
Before LinkedIn, Nishant was at Amazon Web Services, where he worked on Elastic Block Storage (EBS) and authored several patents in distributed storage systems. He began his career at Cisco, contributing to high-performance video backend systems. Nishant holds a Master’s degree in Computer Science from the University of Minnesota, Twin Cities, and has published seven patents across infrastructure, storage, and machine learning platforms.
