Machine Learning Engineer

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.