Staff Engineer, Machine Learning

OVERVIEW OF THE COMPANY

Fox Corporation

Under the FOX banner, we produce and distribute content through some of the world’s leading and most valued brands, including: FOX News Media, FOX Sports, FOX Entertainment, FOX Television Stations and Tubi Media Group. We empower a diverse range of creators to imagine and develop culturally significant content, while building an organization that thrives on creative ideas, operational expertise and strategic thinking.

JOB DESCRIPTION

Under the FOX banner, we produce and distribute content through some of the world’s most iconic and valued brands, including FOX News Media, FOX Sports, FOX Entertainment, FOX Television Stations, and Tubi Media Group. We empower a diverse range of creators to imagine and deliver culturally impactful content, while fostering an environment that thrives on innovation, creativity, and strategic execution.

ABOUT THE ROLE

We are seeking a Staff Engineer to join our Personalization & Recommendations team for Fox’s next-generation Direct-to-Consumer (DTC) streaming platform. This is a hands-on individual contributor role that also provides technical leadership and mentorship to a growing team of data scientists & backend engineers.

You will work on building and scaling recommendation systems, user personas, real-time inference services, and model experimentation frameworks that power personalized user experiences across content surfaces—such as homepage, watch-next, and search.

This role is ideal for engineers who are passionate about solving complex personalization problems at scale and enjoy both coding and leading by example.

A SNAPSHOT OF YOUR RESPONSIBILITIES

• Build, train, and deploy personalization models using real-time and offline user behavior signals

• Mentor junior data scientists and machine learning engineers in modeling techniques, experimentation, and system integration

• Partner closely with Product, Data Engineering, and API teams to translate product needs into scalable ML solutions

• Design and implement modular, low-latency recommendation services integrated into the streaming platform

• Define and evolve best practices for model versioning, A/B testing, and feature store management

• Own the end-to-end lifecycle of personalization models—from research and prototyping to production deployment

• Contribute to incident management, system performance tuning, and ensure SLAs for all user-facing ML endpoints

• Align with US-based teams to ensure timely execution of roadmap items, architectural consistency, and delivery velocity

WHAT YOU WILL NEED

• 8+ years of experience in software engineering and machine learning, with at least 5 years focused on building personalization & recommender systems

• Proven track record of building and deploying ML models for content or product personalization at scale (millions of active users, millions of product items in catalog)

• Strong coding experience in Python, Scala, or Go, with exposure to modern ML and data engineering stacks

• Experience working with distributed systems and streaming architectures (e.g., Kafka, Flink)

• Deep understanding of ML infrastructure, including online/offline training loops, real-time inference, and deployment tools (e.g., Databricks, SageMaker, or AWS Personalize)

• Experience deploying services in cloud environments such as AWS, GCP, or Azure

• Familiarity with vector search engines (e.g., FAISS, Vespa) and semantic retrieval

• Ability to communicate technical concepts clearly and influence architectural decisions across teams

NICE TO HAVE, BUT NOT A DEALBREAKER

• Experience with ad personalization, dynamic creative optimization, or contextual targeting

• Built and scaled recommendation systems for live events or high-traffic scenarios

• Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) in production environments

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Learn more about Fox Tech at https://tech.fox.com 

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We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, gender identity, disability, protected veteran status, or any other characteristic protected by law. We will consider for employment qualified applicants with criminal histories consistent with applicable law.