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About This Role
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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
Full-time or part-time: Full-time
Job title: Data Science Manager
Job Location: Virtual, New York, NY
Job Description:
Reachlocal Inc. is a wholly-owned subsidiary of USA TODAY Co.
Lead a team of data scientists/analysts in the design, development, and deployment of advanced analytics, machine learning, and AI solutions to address complex business problems. Design and deploy predictive models and machine learning algorithms, ensuring model accuracy, scalability, and seamless integration with enterprise systems through collaboration with data engineering teams on efficient data pipeline architecture. Drive organizational data and strategy and partner with business leaders to deliver actionable insights that support strategic decision-making. Establish and enforce data science standards, methodologies, and governance frameworks to maintain data integrity and compliance. Evaluate and implement cutting-edge tools and technologies in AI, machine learning, and big data to maintain a competitive advantage. Coordinate project delivery timelines, resource allocation, and budget management for data science initiatives. Monitor and report on key performance indicators for data analytics programs, ensuring alignment with organizational objectives. Supervises 2 direct reports at a Data Scientist level. 100% Telecommuting permitted.
Minimum Education & Experience Requirements:
Bachelor’s degree in Data Science, Engineering, or a related field, or a foreign equivalent plus 3 years of post-baccalaureate experience in job offered or any leadership role in data science or analytics.
Requires 3 years of experience in:
- Applying descriptive statistics and identifying data trends with machine learning algorithms
- Building and optimizing model performance with R programming and Python
- Designing and implementing deployment environment, scaling the models and resolving issues using AWS, AZURE, Docker cloud technologies
- Evaluating data structure and performing data transformation with R, Python and MySQL
- Preparing analytics reports with ggplot and Rshiny visualization packages to stakeholders and team members to improve KPIs
- Managing and mentoring a team of data scientists and analysts, setting performance metrics and team goals.
100% Telecommuting permitted.
Salary range: $189,259 – $195,000//year
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 $189K-$195K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($192K) sits 9% below the category median. Disclosed range: $189K to $195K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
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: $195K - $195K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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