Job Number: R50031862
Brand: Fox Corporation
Job Type: Engineering, Technology
Location Type: Hybrid
Experience Level: Experienced Hires
Location: Los Angeles, California ; New York, New York
Job Posting Date: March 24, 2026
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
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
#Ll-KD1
#Ll-Hybrid
Learn more about Fox Tech at https://tech.fox.com
#foxtechWe 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.
Pursuant to state and local pay disclosure requirements, the pay rate/range for this role, with final offer amount dependent on education, skills, experience, and location is $74,000.00-130,000.00 annually. This role is also eligible for various benefits, including medical/dental/vision, insurance, a 401(k) plan, paid time off, and other benefits in accordance with applicable plan documents. Benefits for Union represented employees will be in accordance with the applicable collective bargaining agreement.