VP of Engineering, Content Intelligence & Discovery

Fubo•New York, NY

25d•$275,000 - $320,000•Hybrid

About The Position

Fubo is seeking a VP of Engineering to lead our Content Intelligence & Discovery organization. This leader will oversee the technical "brain" of our platform, the systems that ingest, model, and intelligently surface content to millions of users. Reporting to the CTO, you will lead a multi-disciplinary organization that sits at the intersection of high-scale systems engineering and applied machine learning. Your mission is to move beyond baseline heuristics toward sophisticated, high-performance algorithms while maintaining a "you build it, you run it" operational culture. The role involves leading teams focused on Search & Personalization, Knowledge Graph, and strategic collaboration with the Video AI team. The tech stack includes Go, Python, Scala, TypeScript, BigQuery, BigTable, GCS, Apache Kafka, Google Pub/Sub, Apache Beam, Google Dataflow, Apache Airflow, Elasticsearch, Vertex AI, Kubernetes, Redis, React, Vite, Material UI, and Streamlit.

Requirements

Seasoned systems engineer fluent in Data Science, capable of auditing backend architecture and discussing nuances of vector embeddings or ranking loss functions.
Experience running high-traffic production systems with a belief that engineers should be empowered to manage their own production environments.
Driven by metrics and able to translate product needs into technical requirements.
Track record of scaling engineering organizations, mentoring managers, and fostering a culture of excellence in fast-paced environments.
Must be located in NYC and willing to come into the office on a hybrid basis, three times a week (Tuesday, Wednesday, Thursday).

Nice To Haves

Experience with Go/JVM-based backend architecture.
Familiarity with vector embeddings or ranking loss functions.

Responsibilities

Lead the transition to more advanced ML models for discovery, ensuring "precision and recall" of systems to connect users to the right content in milliseconds.
Champion and mentor the adoption of AI-assisted development (LLMs for coding, automated testing, and documentation) to increase velocity and code quality.
Instill a culture of high availability, deep observability, and proactive "ops" management within engineering teams, ensuring full-cycle operational ownership of services.
Oversee the design of resilient, low-latency data pipelines and APIs capable of handling massive scale of live-sports metadata and user-activity data.
Synchronize technical roadmaps between domestic teams and international partners to ensure a cohesive strategy for AI-driven metadata enrichment.