Machine Learning Engineer, FOX Forward Deployed

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

FOX Forward Deployed is an 12-month rotational program that embeds early-career machine learning engineers inside the teams powering FOX’s biggest, most-watched moments.

You will complete two six-month deployments across AI-focused teams supporting streaming, sports, news, monetization, and enterprise data systems. You will contribute directly to production ML systems used at national scale.

This is not a research sandbox. Models must ship. Systems must scale.
You Build It. America Sees It.

ABOUT THE ROLE

As a Machine Learning Engineer in FOX Forward Deployed, you will rotate across two ML-focused teams embedded within core business units across Streaming, Sports, News, FOX One, and platform organizations. You will build, deploy, and monitor models operating inside live production systems.

From sports video intelligence and newsroom AI to ranking, retrieval, and monetization systems, you will work in high-visibility environments where model quality, latency, reliability, and deployment speed directly impact user experience and business performance.

You will operate in an AI-native environment leveraging platforms such as AWS SageMaker and Bedrock, Google Vertex AI, Databricks, Snowflake, ChatGPT, and Claude to accelerate experimentation and production delivery.

A SNAPSHOT OF YOUR RESPONSIBILITIES

• Rotate across two ML-focused teams embedded within operating business units

• Build, train, evaluate, and deploy production machine learning models

• Work with large-scale, real-world datasets and live data streams

• Integrate models into consumer-facing and enterprise systems

• Monitor performance, detect drift, and iterate based on measurable outcomes

• Operate under real constraints around latency, reliability, and scale

WHAT YOU COULD BUILD

• Video Intelligence at Broadcast Scale:​ Develop computer vision systems that analyze live sports and news feeds, detect key moments, and generate AI-powered highlights and metadata used across FOX platforms.
• Search, Ranking, and Retrieval Systems: Train and optimize recommendation and ranking models that determine what millions of viewers see across FOX properties.
• Monetization Optimization Systems:​ Deploy predictive models that improve ad relevance, yield optimization, and engagement across streaming products.
• Enterprise Data and AI Infrastructure:​ Contribute to ML pipelines and platform infrastructure that support retrieval, embeddings, and applied AI systems across consumer and enterprise applications.

WHAT YOU WILL NEED

• Strong foundations in machine learning, statistics, or applied data science

• Experience building and evaluating models through coursework, research, projects, internships

• Proficiency in Python and common ML frameworks

• Demonstrated use of AI-assisted tools to accelerate ML workflows

• Ability to explain how you validated model quality using metrics, bias checks, reproducibility controls.

• Curiosity about how models behave in production environments

• Bias toward experimentation and measurable outcomes

FOX Forward Deployed is intentionally small and selective. Participants are expected to operate as contributing ML engineers from day one.

HOW WE EVALUATE BUILDERS

We evaluate builders by what they’ve shipped.

You will be asked to:

• Share one ML artifact such as repository, demo, or paper

• Explain the problem the model solved

• Describe the evaluation metrics you chose and why

• Detail one real constraint or tradeoff

• Explain how you used AI tools and how you verified their outputs

NICE TO HAVE, BUT NOT A DEALBREAKER

• Experience deploying models into production systems

• Exposure to recommendation systems, ranking, or personalization

• Familiarity with data pipelines or distributed systems

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