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About This Role
About Us:
TMB is the community\-driven entertainment company engaging more than 250 million consumers worldwide across streaming TV, social media, web, and print. Our portfolio of brands including FailArmy, Family Handyman, People Are Awesome, Reader's Digest, Taste of Home, The Healthy, and The Pet Collective, is powered by content that's inspired and created by our fans. Together our community sparks curiosity, fuels laughter, and inspires people to live big, full, fantastic lives. Learn more about our brands, our data\-driven marketing solutions, our award\-winning licensing services, and much more at trustedmediabrands.com.
Location:
We have offices in New York, Los Angeles, Milwaukee, and Gurgaon. This position will be based in the United States and can work remotely.
About the role:
TMB is looking for a Director of Data Science \& AI to lead the team and efforts to turn data into decisions, deliver scalable intelligence that drives personalized experiences and business outcomes, and lean into AI and Automation where it makes sense. You understand how business users think, what friction looks like in real workflows, and how to communicate technical solutions in ways that land with non\-technical users.
Your primary mission is to bridge the gap between what’s technically possible and what’s practically useful. This is a hands\-on leadership role: you’ll set the technical direction, but you’ll also be in the data, in the model reviews, and building alongside your team when it counts. We need someone fluent in Google Cloud Platform (BigQuery, Vertex AI, and broader GCP ecosystem), has strong data science capabilities and the grit to take ideas from research to production.
You are a fast learner who is comfortable in ambiguity, energized by solutioning, and motivated by seeing your work drive real adoption across the organization. You'll spend your time championing smarter ways of working and helping both technical and non\-technical teammates integrate AI into their everyday work, not as a disruption, but as a natural extension of how they already operate.
Your day\-day: (aka Responsibilities)* Own the data science roadmap for the organization from core statistical modeling and experimentation forecasting, segmentation and applied AI where it adds real business value
- Lead, mentor, and stay hands\-on with a small team of data scientists: writing code and reviewing methodology, not just roadmaps
- Oversee the design of robust, production\-grade solutions utilizing core cloud systems, vector databases, and agentic workflows
- Champion the adoption and continuous improvement of AI development tools across technical teams
- Scale MLOps/LLMOPs practices, ensuring data observability, governance, model safety, and ethical AI compliance
- Collaborate cross\-functionally with Business Intelligence, Data Engineering, Analytics Engineering, Development, and Product teams to drive technical innovation, prioritize initiatives, ship MVPs fast and iterate based on usage data
- Design and run rigorous experiments to validate hypotheses and guide business decisions
- Translate statistical and technical work into business language for executive stakeholders and make the case for where data science investment should go next
- Define success metrics for internal tools and AI initiatives, and track adoption, usage, and business impact over time
- Document tools, workflows, and best practices to scale impact beyond individual engagements
- Stay informed on industry trends, viral content, and emerging platforms to identify new opportunities
You have: (aka Qualifications)* 7\+ years in data science, including leading or managing a technical team
- Strong foundation in core data science
- Deep, hands\-on fluency in Google Cloud Platform: BigQuery, Vertex AI, and building/deploying production models in a GCP environment
- Proficiency deploying custom Gemini Enterprise Agents via Agent Engine
- Fluency in AI tools such as Claude, CoPilot and ChatGPT
- Strong Python and SQL skills and experience in applied AI
- A bias for action
About this team:
The Data Science team is a small but mighty group sitting at the center of the organization. We're a centralized team, which means we work across nearly every department: from Marketing and Creative to BI, Engineering, Product, and beyond. If there's a workflow to improve, a tool to build, or a data problem worth solving, chances are we're involved.
Our work is practical by design. We build internal tools and data products that make real work easier, not to chase trends, but because the right solution at the right moment can meaningfully change how an entire team operates. We use AI where it genuinely helps, and we're skeptical of hype for its own sake. That pragmatic instinct is part of what makes us effective.
As a team, we move fast and stay scrappy. We're not a big org with lengthy approval chains. We prototype, iterate, and ship. We collaborate constantly, both within the team and across the business, and we're at our best when we're working alongside stakeholders to understand their problems firsthand rather than solving in a vacuum.
If you're joining us, you're joining a team that values curiosity, initiative, and craft. Our team genuinely believes the most impactful data work happens when technical depth meets real business context.
Our Benefits:
We value our people and offer a collaborative and engaging culture. As a Trusted Media Brands employee, you will enjoy work/life balance, generous time off and comprehensive benefits and programs. Learn more about what life is like working at Trusted Media Brands at https://www.trustedmediabrands.com/careers/.
*Trusted Media Brands embraces inclusivity and values our diverse community. We are committed to building a team based on qualifications, merit and business need. We are proud to be an equal opportunity employer and do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.*
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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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Trusted Media Brands, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Trusted Media Brands AI Hiring
Trusted Media Brands has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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