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 the
Personalization & Recommendations (PnR)
team and help drive the evolution of
personalized content discovery
across our streaming products. In this role, you’ll be a
hands-on contributor
responsible for designing, building, and deploying
ML models for recommendations, ranking, and semantic search
, and ensuring they evolve through
continuous learning and experimentation
.
You will work at the intersection of
ML 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 have
built 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 scalable
recommendation and personalization models
(ranking, re-ranking, user embeddings, semantic retrieval)
Own the full model lifecycle: from
data preparation
,
training
, and
evaluation
, to
versioning
,
deployment
, and
monitoring
Develop and maintain
continuous training loops
and
model refresh strategies
for dynamic personalization
Set up and interpret
A/B experiments
to optimize model performance and user engagement
Collaborate with data engineers, MLOps teams, and product managers to ensure models integrate seamlessly into
real-time and batch inference pipelines
Leverage platforms like
Databricks, MLflow
, and
feature stores
to streamline model experimentation and reproducibility
Apply
LLMs 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 in
machine learning, applied data science
, or related fields, with a strong focus on
recommendation systems or personalization
Demonstrated experience in
developing and deploying ML models
into production environments
Deep understanding of
ranking systems, user behavior modeling
, and evaluation techniques (e.g., NDCG, AUC, MAP, CTR)
Proficient in
Python
and ML libraries like
PyTorch, TensorFlow
, and frameworks such as
Transformers or LightGBM
Experience with
Databricks
, Spark, or similar big data platforms for large-scale model training and data processing
Familiarity with
model versioning, feature stores, experiment tracking
, and
MLflow
Strong grasp of
A/B testing design
, analysis, and interpreting results for iterative model improvements
Experience with
LLM-based pipelines
,
semantic search
, or
vector 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 building
AI agents
,
LangChain
, or
workflow automation frameworks
for model experimentation
Exposure to
real-time inference systems
and streaming architectures (Kafka, Flink)
Experience working on personalization systems at
scale
, 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.
