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About This Role
\|Current USA TODAY Co. Employees\- Please ensure you are using our abbreviated process on the internal Careers site by logging into Dayforce through MyApps\|
USA TODAY Co., Inc. is a diversified media company with expansive reach at the national and local level dedicated to empowering and enriching communities. We seek to inspire, inform, and connect audiences as a sustainable, growth focused media and digital marketing solutions company. Through our trusted brands, including the USA TODAY NETWORK, comprised of the national publication, USA TODAY, and local media organizations, including our network of local properties, in the United States, and Newsquest, a wholly\-owned subsidiary operating in the United Kingdom, we provide essential journalism, local content, and digital experiences to audiences and businesses. We deliver high\-quality, trusted content with a commitment to balanced, unbiased journalism, where and when consumers want to engage. Our digital marketing solutions brand, LocaliQ, supports small and medium\-sized businesses with innovative digital marketing products and solutions. USA TODAY Co. open roles are featured on various external job boards. When applying to a position at USA TODAY Co., you should be completing an application on USA TODAY Co. Careers via Dayforce. Job postings directing you to complete an application on other external sites may not be valid. If you are a California resident, you acknowledge that by applying for a job with us, this California Job Applicant Privacy Notice will apply to our collection, use, and disclosure of your personal information. To connect with us, visit www.usatodayco.com
News Data Automation Lead, Content AI
The USA TODAY NETWORK is seeking a News Data Automation \& AI Workflow Lead to help design, build, and scale the next generation of data driven and AI assisted journalism across the nation’s largest local news organization.
This role is ideal for a well rounded and innovative newsroom technologist — someone who is deeply skilled in data automation and scraping, but who also brings strong news judgment, comfort with emerging AI tools, and the ability to think beyond a single pipeline or use case. The successful candidate will help lead the development of automated workflows, AI assisted tools, and newsroom ready products that turn public data sources into the raw material our journalists need to do impactful work.
This is a hands on role that blends technical execution, experimentation, and leadership. On some days, the work will involve building or troubleshooting data pipelines and automation systems. On others, it will focus on ideation, product testing, mentoring, workflow design, and translating technical concepts into clear guidance for editorial partners.
This role is remote and can be based anywhere in the US, except for Alaska, Hawaii and Wyoming.
Why This Role Matters
The USA TODAY Network serves communities across hundreds of markets. Many of the most important stories in local journalism begin with a signal hidden in data or surfaced through automation. This role exists to help the Network find those signals faster, build smarter tools around them, and ensure journalists can act on them confidently and consistently.
By combining data automation expertise with AI experimentation, newsroom collaboration, and workflow leadership, this role helps ensure technology meaningfully strengthens local journalism — not just produces output.
Who You Are
- A strong technical practitioner with hands on newsroom experience in data automation and scraping, who is also curious, adaptable, and comfortable working across disciplines.
- Someone who enjoys moving between building, explaining, and improving systems, not just writing code in isolation.
- A collaborator who can translate technical language into newsroom friendly terms and help others understand how tools can support their work.
- Comfortable operating in a fast moving environment where priorities evolve and experimentation is encouraged.
- Grounded in journalistic values, with a clear understanding of accuracy, ethics, and editorial responsibility.
What Success Looks Like
In the first 90 days, you will:
- Audit existing data automation and AI assisted workflows and recommend which should be maintained, narrowed, redesigned or retired.
- Stabilize and document at least one high value existing pipeline or internal tool.
- Begin building or redesigning at least one priority workflow around a timely, high value public dataset, including a clear plan for AI assisted components where appropriate.
Within six months, you will:
- Deliver a focused set of reliable, documented pipelines and/or tools centered on high priority local signals.
- Create newsroom ready output formats and utilities that make those signals actionable quickly and consistently for multiple markets.
- Help shift resources away from low value recurring automation and toward better differentiated data and AI driven opportunities.
Within 12 months, you will:
- Establish a durable portfolio of data driven and AI assisted workflows that support the Network’s automation and audience growth strategy.
- Demonstrate that those workflows are producing actionable journalism opportunities, meaningful audience value and clear time savings for local newsrooms.
- Build systems that are resilient, scalable and useful across multiple markets without requiring constant reinvention.
Responsibilities
- Build, maintain, and improve data automation pipelines, scrapers, APIs, and AI assisted workflows that support high value newsroom use cases.
- Identify, acquire, and structure high impact datasets and signals that can drive timely, differentiated coverage across local markets.
- Design and implement AI enabled tools and templates that help journalists move faster while maintaining high editorial standards.
- Work directly with editorial leaders, reporters, and automation teams to translate newsroom needs into technical solutions.
- Prototype, test, and refine new automation and AI workflows, balancing speed, reliability, and editorial usefulness.
- Troubleshoot automation, data, or tool issues as they arise, providing clarity and guidance to partners across the newsroom.
- Help evaluate existing automation efforts and recommend which should be expanded, refined, redesigned, or retired based on value and impact.
- Develop newsroom ready outputs that may include story drafts, alerts, tip sheets, reporting leads, dashboards, or other editorial tools, depending on the use case.
- Mentor and support journalists and teammates using automation and AI tools, helping raise overall technical fluency and confidence.
- Clearly document workflows and systems so they are understandable, resilient, and scalable beyond a single owner.
- Stay current on developments in AI, data automation, and newsroom technology, and help envision future use cases that responsibly support journalism.
Requirements
- Bachelor’s degree in journalism, communications, data science, computer science or a related field. Equivalent work experience welcomed.
- 3\-5 years of experience in journalism, data journalism, newsroom automation, computational journalism, software development, or a closely related field.
- Strong technical skills, including experience with Python, scraping, APIs, data cleaning, and workflow automation.
- Demonstrated ability to work with messy, real world data and turn it into reliable, usable systems.
- Strong news judgment and a clear understanding of what makes a local data signal genuinely reportable, useful and differentiated.
- Familiarity with AI tools and the broader generative AI landscape, including benefits, risks, and responsible use in journalism.
- Strong communication skills, including the ability to document technical processes and explain them to non technical partners.
- Proven ability to manage multiple projects and priorities in a deadline driven environment.
- Experience in a local newsroom or in work serving multiple local markets.
- Experience building or maintaining automated journalism workflows end to end.
- Familiarity with CMS workflows and digital publishing operations.
- Experience designing alerts, triggers or threshold\-based systems for editorial use.
- Experience working with public records or watchdog\-style datasets such as inspections, enforcement actions, labor notices, court records, licensure data or government records.
- Experience using AI tools to support data processing, classification, summarization, or workflow efficiency.
Application Instructions
We are eager to learn more about you and how you fit this role. When you apply, don’t limit your upload to a resume; show us what you’ve done. To do so, put together a single document file that includes the following, in this order:
1\. Your resume – one to two pages.
2\. A cover letter outlining how you would approach the role.
3\. Links to no more than three published stories, writing samples.
It is important that these items be assembled into a single document and uploaded in PDF format. Completing these steps will ensure that your application receives the highest consideration.
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The hourly rate for this role will range between $24\.66 and $50\.48\. Base compensation is reflective of many factors, including, but not limited to, the market in which one lives/works, individual education level, skills, certifications and experience. Note: variable compensation is not reflected in these figures and based on the role, may be applicable.
USA TODAY Co., Inc. is a proud equal opportunity employer committed to building and maintaining a diverse workforce. As such, we will consider all qualified applicants for employment and do not discriminate in connection with employment decisions on the basis of an applicant or employee’s race, color, national origin, ethnicity, ancestry, citizenship status, sex, gender, gender identity, gender expression, religion, age, marital status, personal appearance (including height and weight), sexual orientation, family responsibilities, physical or mental disability, medical condition, pregnancy status (including childbirth, breastfeeding or related medical conditions), education, genetic characteristics or information, political affiliation, military or veteran status or other classifications protected by applicable federal, state and local laws in the jurisdictions where USA TODAY Co. employs employees. In addition, USA TODAY Co., Inc. will provide applicants who require a reasonable accommodation, as a result of an applicant’s disability or religion, to complete this employment application and/or any other process in connection with an individuals’ application for employment with USA TODAY Co., Inc. Applicants who require such accommodation should contact USA TODAY Co., Inc.’s Recruitment Department at [email protected]. Applicants must be authorized to work in the applicable location. Applications from outside these regions will be removed from our system after submission.
Salary Context
This $49K-$104K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At USA TODAY Co., 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($76K) sits 58% below the category median. Disclosed range: $49K to $104K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
USA TODAY Co. AI Hiring
USA TODAY Co. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $104K - $104K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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