VP of Engineering, Content Intelligence & Discovery

About the role

You will own the technical vision and execution for the Content Intelligence & Discovery organization, defining how Fubo surfaces the right live TV content to the right user at the right time. You will architect and manage the systems that ingest, model, and intelligently distribute content metadata to millions of global viewers across search, personalization, and knowledge graph platforms. You will partner with product and data science to translate business objectives into scalable engineering outcomes while maintaining a high-performance, reliability-focused culture. You will lead the design of the content intelligence brain that powers discovery, ensuring algorithmic precision aligns with user retention and engagement goals. You will mentor engineers in advanced ML practices and champion the operational ownership of services from development through production. You will act as the primary technical liaison between domestic engineering teams and international partners to standardize AI-driven content workflows. You will ensure that the underlying infrastructure meets the demands of live sports and entertainment at scale without compromising speed or accuracy.

Key facts

What you'll do

Lead the roadmap for search, personalization, and semantic search to improve content discovery quality and user retention across Fubo platforms.

Build and evolve the Knowledge Graph as the foundational abstraction layer that normalizes massive metadata and real-time sports statistics into a unified, queryable graph.

Partner with the Video AI organization in India to operationalize computer vision and NLP outputs, ensuring AI-driven enrichment feeds directly into the Knowledge Graph and SNP pipelines.

Oversee the design and operation of high-scale data pipelines and APIs using stream and batch processing frameworks to handle live-sports metadata and user-activity data with low latency.

Define and enforce architectural standards for resilient, performant systems that support Elasticsearch and Vertex AI within a Kubernetes and Redis-based infrastructure.

Champion AI-assisted development practices, including LLMs for coding, automated testing, and documentation, to accelerate delivery and improve code quality across engineering teams.

Instill a "you build it, you run it" culture of full-cycle operational ownership, focusing on high availability, deep observability, and proactive service management.

Coordinate technical roadmaps between domestic and international teams to ensure cohesive execution of AI-driven content intelligence initiatives globally.

Guide the adoption of modern data storage and processing technologies such as BigQuery, BigTable, GCS, Apache Kafka, Google Pub/Sub, and Apache Beam.

Mentor and grow a multi-disciplinary organization at the intersection of systems engineering and applied machine learning, aligning technical execution with product goals.

Requirements

Candidates must be based in New York, NY, and willing to work a hybrid schedule with three days in the office (Tuesday, Wednesday, Thursday).

You must have extensive experience designing and operating large-scale distributed systems that handle high-volume real-time data.

You must demonstrate deep expertise in building and managing knowledge graphs and semantic normalization of complex metadata.

You must have a strong background in search and personalization algorithms, including ranking models, collaborative filtering, and semantic search.

You must have hands-on experience with AI and ML pipelines, including the integration of computer vision and NLP outputs into production systems.

You must be proficient in modern backend languages such as Go, Python, and Scala, and comfortable building services that run at scale.

You must have experience managing data infrastructure including BigQuery, BigTable, GCS, Kafka, Pub/Sub, and stream processing frameworks.

You must have a track record of leading high-performing engineering teams in a fast-growth, consumer-facing environment.

Nice to have

Experience with Elasticsearch and Vertex AI within large-scale production environments.

Familiarity with Spotify Scio and Google Dataflow for batch and stream processing workflows.

Exposure to live sports data models and real-time metadata challenges in media and entertainment.

Practical notes

This role is a New York City based HYBRID position. Candidates must be located in NYC, and willing to come into the office on a hybrid basis, three times a week (Tuesday, Wednesday, Thursday).