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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. To connect with us, visit www.usatodayco.com
USA TODAY PLAY is our casual entertainment platform (crosswords, puzzles, comics, horoscopes, quizzes, and more) built on a strong legacy foundation and positioned to become a major subscription revenue driver for the USA TODAY Network. We're in the early stages of a significant shift toward subscription economics, and this role sits at the center of that transformation.
We're looking for a Senior Marketing Strategist who can own the full subscriber lifecycle for PLAY, from anonymous visitor through engaged, retained subscriber. You'll build the strategic framework and hands\-on programs that grow the funnel, create meaningful conversion pressure, drive habitual engagement, and reduce churn. This is not a campaign execution role. You'll be expected to think in systems and programs: designing triggered engagement loops, calibrating friction across the funnel, and connecting paid, owned, and product channels into a cohesive growth engine grounded in subscriber economics.
You'll report to the Senior Director of Consumer Journeys and partner closely with Product, Data, Activation, Creative, and the PLAY business unit. You'll have strong strategic support and a team environment that values rigorous thinking, experimentation, and financially grounded decision\-making.
Key Responsibilities
Lifecycle Strategy \& Execution
Design and operate the end\-to\-end lifecycle program for PLAY. Lead acquisition, registration\-to\-subscription conversion, onboarding, recurring engagement, upsell, and winback. Build behavior\- and engagement\-based triggered touchpoints that create habit formation and drive purchase intent across email, push, in\-app, and web channels.
Funnel Growth \& Conversion Optimization
Own the known\-to\-paid funnel. Develop and iterate paywall strategy, offer sequencing, and registration gates that balance access with conversion pressure. Partner with Product to optimize user journeys, stacked subscription experiences, and account management touchpoints.
Experimentation \& Decisioning
Maintain a structured testing backlog across creative, paywall friction, offer constructs, landing pages, overlays, and lifecycle touchpoints. Run disciplined A/B and multivariate tests, publish clear readouts, and scale winners into repeatable playbooks.
Churn Mitigation \& Retention
Build and optimize winback flows, including dynamic offer logic, onsite intercepts, and coordination with account management and call center policies. Identify leading indicators of churn risk and design proactive engagement interventions.
Paid Media \& Channel Orchestration
Inform and help orchestrate paid acquisition strategy in coordination with owned channels, ensuring CAC efficiency and alignment with LTV targets. Define targeting and suppression logic within the CDP for real\-time, segment\-level decisioning across channels.
Cross\-Functional Partnership
Work within the PLAY–Consumer operating model to align Product, Data, Activation, and Creative on shared goals. Communicate strategy, performance, and progress to senior stakeholders with clarity and precision.
Qualifications
- 5\+ years in subscription growth or lifecycle marketing, with experience in gaming, entertainment, or media preferred
- Hands\-on experience building paywall strategy, dynamic offer flows, and churn mitigation programs (stop\-save, winback) at scale
- Proven track record designing and scaling multi\-channel lifecycle programs — particularly behavior\-based triggered journeys — that deliver measurable improvements in subscriber growth, conversion, and LTV
- Proficiency with A/B testing and experimentation frameworks, with the ability to translate results into scalable rollout playbooks
- Working knowledge of marketing technology ecosystems (e.g., Salesforce Marketing Cloud, Braze, CDPs, CRM platforms) and experience applying personalization and segmentation at scale
- Familiarity with paid media best practices and how acquisition channels connect to lifecycle economics (CAC, LTV, payback period)
- Strong analytical judgment — able to independently assess performance data, identify patterns and tradeoffs, and convert quantitative signals into strategic recommendations
- Strong cross\-functional collaboration skills, with the ability to align multiple teams around shared outcomes within a matrixed operating model
\#LI\-REMOTE
\#LI\-NR2
The annualized base salary for this role will range between $100,000 and $110,000\. 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 Recruit@usatodayco.com. 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 $100K-$110K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($105K) sits 37% below the category median. Disclosed range: $100K to $110K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
USA TODAY Co. AI Hiring
USA TODAY Co. has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, US, New York, NY, US. Compensation range: $52K - $114K.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% above the national median.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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