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About This Role
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
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We 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 BUILDERS
We 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
\#Ll\-KD1
\#Ll\-Hybrid
Learn more about Fox Tech at https://tech.fox.com
\#foxtech*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.*
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.
Salary Context
This $74K-$130K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Fox Corporation, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($102K) sits 53% below the category median. Disclosed range: $74K to $130K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Fox Corporation AI Hiring
Fox Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $130K - $130K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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