Software Engineering Manager, Feature AI Platform

LinkedIn•Sunnyvale, CA

1d•Hybrid

About The Position

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. The team shapes the future of AI with the state-of-the-art Feature Platform, which empowers AI Users to effortlessly create, compute, store, consume, monitor, and govern feature data within online, offline, and nearline environments, optimizing the process for model training, model serving, and candidate retrieval. As a leader in the team, you'll drive technical direction across the online, offline, and nearline spaces at scale (millions of QPS, multi-terabytes of data, etc), developing and refining the infrastructure necessary to transform data into valuable features. Utilizing leading open-source technologies like Spark, Beam, and Flink and more, you will play a crucial role in processing and structuring feature data, ensuring its most optimal storage, and serving feature data with high performance. The platform is used by AI practitioners at the company to generate and serve features for major products at the company: Feed, Search, Trust, Recruiter, Jobs, etc. You'll explore and innovate within online/offline/nearline data flows — spanning ingestion, transformation, sharding, and materialization — at massive scale (millions of QPS, multi-TB datasets) Data hydration pipelines: build and scale pipelines that materialize computed features into KV stores for real-time serving Index building for retrieval engines: consume from upstream data sources (streams, batch, feature stores) to build sharded, queryable indexes at scale. Design sharding schemes that balance load, latency, and resource cost across retrieval index shards Consistency & freshness: ensure hydrated/indexed data stays consistent and fresh between source-of-truth and serving layer Operational scale: manage pipelines/indexes serving millions of QPS with strict SLAs, manage capacity and cost attribution across multiple tenants.

Requirements

BA/BS Degree in Computer Science or related technical discipline, or equivalent practical experience.
1+ year(s) of management experience or 1+ year(s) of staff level engineering experience with management training
5+ years of industry experience in software design, development, and large-scale software engineering
Experience programming in object-oriented languages such as Java, C++, Python, Go or Rust.
Hands on experience developing distributed systems, databases, large scale data systems

Nice To Haves

MS or PhD in Computer Science or related technical discipline
2+ years of hands-on software engineering/technical management and people management experience
7+ years industry experience in software design, development, and algorithm related solutions.
5+ years programming experience in languages such as Java, C++, Python, Go, or Rust.
Experience in architecting, building, and running large-scale distributed systems
Experience with industry, opensource, and/or academic research in technologies such as Hadoop, Spark, Kubernetes, gRPC, Apache Kafka, Pinot, Flink, or Venice
Experience working with search and/or recommender systems or other similar large-scale distributed systems.
Experience building ML applications, e.g. LLM serving, GPU serving, feature engineering

Responsibilities

Lead, coach and manage core team of engineers working on building the infrastructure.
Participate with senior management in developing a long-term technology roadmap for the team and company.
Have the ability to dive deep into technical discussions to challenge the status quo, and steer the team in the right direction/to push the envelope.
Communicate and collaborate effectively with stakeholders across engineering and business leadership.
Help the team realize their potential by setting clear expectations, openly evaluating performance, upholding accountability, and providing challenges to stretch their skills.
Drive a culture of operational excellence.
Lead the team into defining performance goals, metrics and building the infrastructure and tooling necessary to maintain a high quality bar and detect issues in real time.
Create an inclusive work environment that fosters autonomy, transparency, innovation and learning, while holding a high bar for quality.