JOB DESCRIPTIONWe are seeking a Machine Learning Engineer I to join our growing team focused on building and deploying machine learning, Large Language Model, and Generative AI systems across FOX platforms.
In this role, you will contribute to production ML systems that support streaming, sports, news, monetization, enterprise data, and internal AI applications. You will work alongside senior engineers, data scientists, product leaders, and platform teams to build models, pipelines, and AI-powered workflows that operate at scale.
You will gain hands-on experience across the full machine learning lifecycle, including data preparation, model development, evaluation, deployment, monitoring, and iteration. This is an opportunity to apply strong ML fundamentals to real-world systems where model quality, latency, reliability, and measurable outcomes matter.
You will operate in an AI-native environment leveraging platforms such as AWS SageMaker and Bedrock, Google Vertex AI, Databricks, Snowflake, ChatGPT, Claude, and modern ML frameworks to accelerate experimentation and production delivery.
A SNAPSHOT OF YOUR RESPONSIBILITIES- Build, train, evaluate, and deploy machine learning models under the guidance of senior engineers
- Support the development of ML pipelines for training, fine-tuning, deployment, and monitoring
- Work with large-scale, real-world datasets across consumer, content, and enterprise systems
- Contribute to LLM, Generative AI, retrieval, ranking, recommendation, and personalization use cases
- Assist in building agentic workflows that allow AI systems to interact with tools, APIs, and internal platforms
- Evaluate model performance using appropriate metrics, validation methods, and reproducibility practices
- Monitor deployed models for quality, reliability, drift, latency, and business impact
- Collaborate with cross-functional teams to integrate AI capabilities into FOX applications
- Participate in code reviews, documentation, testing, and system design discussions
- Stay current on emerging ML, Generative AI, and applied AI techniques
WHAT YOU WILL NEED- Strong foundation in machine learning, statistics, computer science, or applied data science
- Experience building and evaluating ML models through coursework, research, internships, projects, or professional experience
- Proficiency in Python and common ML frameworks such as PyTorch, TensorFlow, JAX, or scikit-learn
- Familiarity with model evaluation, validation metrics, bias checks, and reproducibility practices
- Exposure to LLMs, prompt engineering, embeddings, vector databases, or retrieval-augmented generation
- Understanding of software engineering fundamentals, including version control, testing, and working with APIs
- Demonstrated use of AI-assisted tools to accelerate technical workflows while validating outputs
- Curiosity about how models behave in production environments
- Ability to communicate technical concepts clearly to technical and non-technical audiences
- Bias toward experimentation, measurable outcomes, and continuous learning
- Collaborative mindset and ability to work in a fast-paced, cross-functional environment
NICE TO HAVE, BUT NOT A DEALBREAKER- Experience deploying models into production systems
- Exposure to recommendation systems, ranking, personalization, or search
- Familiarity with cloud platforms or ML infrastructure such as AWS, GCP, Azure, Databricks, or Snowflake
- Experience with LLM orchestration frameworks, function calling, or tool use
- Familiarity with data pipelines, distributed systems, or streaming data
- Experience with multimodal models involving text, vision, audio, or video
- Contributions to open-source projects, technical demos, research, or applied ML products
HOW WE EVALUATE BUILDERSWe evaluate builders by what they have created, tested, and learned from.
Candidates may be asked to:
- Share an ML artifact such as a repository, demo, project, paper, or deployed system
- Explain the problem the model or system was designed to solve
- Describe the evaluation metrics chosen and why they mattered
- Discuss one technical constraint, tradeoff, or failure mode
- Explain how AI tools were used and how their outputs were verified
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Learn more about Fox Tech at https://tech.fox.com
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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 an annual discretionary bonus, 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.
View more detail about FOX Benefits.
