Machine Learning Engineer

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

OVERVIEW OF THE COMPANY

Fox Corporation is home to industry-leading brands including FOX News Media, FOX Sports, FOX Entertainment, FOX Television Stations, and Tubi Media Group. We combine innovative technology, deep data insights, and world-class content to shape the future of digital entertainment. Our DTC platforms are built to deliver highly personalized, scalable user experiences to millions of global users.

ABOUT THE ROLE

FOX Corporation is looking for a SDE (L2), ML /Senior Engineer, ML to join thePersonalization & Recommendations (PnR) team and help drive the evolution ofpersonalized content discovery across our streaming products. In this role, you’ll be ahands-on contributor responsible for designing, building, and deployingML models for recommendations, ranking, and semantic search, and ensuring they evolve throughcontinuous learning and experimentation.

You will work at the intersection ofML model development, production engineering, and data-driven experimentation, collaborating with cross-functional teams to ensure scalable, performant, and personalized experiences. This role is ideal for engineers who havebuilt and iterated on production-grade personalization systems and thrive on both deep technical challenges and business impact.

A SNAPSHOT OF YOUR RESPONSIBILITIES

• Design and build scalablerecommendation and personalization models (ranking, re-ranking, user embeddings, semantic retrieval)

• Own the full model lifecycle: fromdata preparation,training, andevaluation, toversioning,deployment, andmonitoring

• Develop and maintaincontinuous training loops andmodel refresh strategies for dynamic personalization

• Set up and interpretA/B experiments to optimize model performance and user engagement

• Collaborate with data engineers, MLOps teams, and product managers to ensure models integrate seamlessly intoreal-time and batch inference pipelines

• Leverage platforms likeDatabricks, MLflow, andfeature stores to streamline model experimentation and reproducibility

• ApplyLLMs and AI agents to improve personalization workflows and accelerate ML development pipelines

• Contribute to architecture decisions for personalization services and model serving infrastructure

• Mentor and provide technical guidance to junior data scientists and ML engineers, conducting code reviews, sharing best practices, and supporting their growth in areas such as model development, experimentation, and productionization

WHAT YOU WILL NEED

• At least 3-7 years of experience inmachine learning, applied data science, or related fields, with a strong focus onrecommendation systems or personalization

• Demonstrated experience indeveloping and deploying ML models into production environments

• Deep understanding ofranking systems, user behavior modeling, and evaluation techniques (e.g., NDCG, AUC, MAP, CTR)

• Proficient inPython and ML libraries likePyTorch, TensorFlow, and frameworks such asTransformers or LightGBM

• Experience withDatabricks, Spark, or similar big data platforms for large-scale model training and data processing

• Familiarity withmodel versioning, feature stores, experiment tracking, andMLflow

• Strong grasp ofA/B testing design, analysis, and interpreting results for iterative model improvements

• Experience withLLM-based pipelines,semantic search, orvector similarity systems (e.g., FAISS, Vespa) is a plus

• Comfort working in cloud-native environments such as AWS or GCP

NICE TO HAVE, BUT NOT REQUIRED

• Experience using or buildingAI agents,LangChain, orworkflow automation frameworks for model experimentation

• Exposure toreal-time inference systems and streaming architectures (Kafka, Flink)

• Experience working on personalization systems atscale, particularly for high-traffic applications or live events
  Contributions to open-source ML tools or research in personalization-related fields

#LI-SS1

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.