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About This Role
### The Opportunity:
The AI Platform team is seeking an AI Platform Engineer to design and build the foundational systems, APIs, and tooling that empower our engineering and product teams to work efficiently. In this role, you will architect the AI\-powered infrastructure layer that development and product teams rely on—ranging from internal developer tooling and code generation pipelines to AI\-assisted product lifecycle systems. This position supports the Tech \& Shopping (T\&S) division of Ziff Davis (NASDAQ: ZD), which operates digital media and software brands including CNET, RetailMeNot, Mashable, PCMag, Lifehacker, and Spiceworks. This is your chance to lead high\-impact projects! Do you want to see the direct impact of your performance and thrive both personally and professionally? This role operates on a remote work arrangement with no regular travel requirements.
### Key Responsibilities:
The individual in this position will own the lifecycle of internal AI APIs, SDKs, and developer tooling to abstract LLM complexity and establish reliable, production\-grade primitives. You will build and maintain CI/CD\-integrated AI systems, including code review automation, test generation pipelines, and agentic systems capable of executing multi\-step engineering tasks. Additionally, you will drive product development acceleration by integrating AI capabilities directly into existing collaborative workflows like Jira, GitHub, and Slack.
- Design evaluation frameworks and automated testing pipelines to ensure AI system outputs meet quality thresholds.
- Build observability tooling to monitor model behavior, token costs, latency, and production output quality.
- Establish shared prompt libraries, model routing logic, and reusable context management systems.
- Define and enforce engineering standards for AI system development across the division.
- Provide technical guidance and conduct architectural reviews of AI features built by product teams.
### Job Qualifications:
The ideal candidate brings deep software engineering fundamentals paired with a clear, collaborative approach to raising the technical bar across engineering cohorts. You possess strong capabilities in system design, API design, and distributed infrastructure management.
- 5\+ years in software engineering, platform engineering, or infrastructure, with at least 2 years building production AI/ML systems.
- Deep experience with LLM APIs and a strong understanding of how these systems behave under real production load.
- Experience building internal developer platforms, tooling, or shared infrastructure used by other engineers.
- Proficiency in Python and at least one compiled language; comfortable across the full backend stack.
- Preferred: Familiarity with agentic frameworks, RAG architectures, vector databases, or infrastructure\-as\-code (Terraform).
### About Tech \& Shopping
Our portfolio unites the most trusted brands in tech, shopping, savings, and lifestyle to help people and businesses thrive. The CNET Group stands as the leader in tech media, offering expert editorial, insights, communities, and solutions that reach 81 million unique visitors each month. Complementing this, Ziff Davis Shopping serves as the most trusted purchase companion, delivering exceptional user and partner experiences. We connect brands to large, contextually\-aligned audiences and help users find the best offers, cash back rewards, seasonal deals, and expert content for all their shopping needs. Together, we provide a comprehensive destination for expert guidance, from critical tech decisions to everyday purchase solutions.
### About Ziff Davis
Ziff Davis (NASDAQ: ZD) is a vertically focused digital media and internet company whose portfolio includes leading brands in technology, shopping, gaming and entertainment, connectivity, health, cybersecurity, and martech. Today, Ziff Davis is focused on seven key verticals – Technology, Connectivity, Shopping, Entertainment, Health \& Wellness, Cybersecurity and Marketing Technology. Its brands include IGN, Mashable, RetailMeNot, PCMag, Humble Bundle, Spiceworks, Ookla (Speedtest), RootMetrics, Everyday Health, BabyCenter, Moz, iContact and Vipre Security.
### Our Benefits
Ziff Davis offers competitive salaries in addition to robust, health and wellness\-focused benefits, including comprehensive medical, dental, and vision coverage, as well as life and disability benefits. We offer amazing benefits! Our employees enjoy Flexible Spending Accounts (FSAs), a 401(k) with company match, and an Employee Stock Purchase Plan.
We are committed to work\-life balance with Flexible Time Off, Volunteer Time Off, and paid holidays. We offer family building and caregiving support and generous Family Care and Parental leave, when you need it. We also provide Fitness Reimbursement and access to wellness programs, ensuring our team stays healthy both physically and mentally.
At Ziff Davis, we remain dedicated to creating an environment where everyone feels valued, respected, and empowered to succeed. We offer Employee Resource Groups, company\-sponsored events, and regular opportunities for professional growth through educational support, mentorship programs, and career development resources. Our employees are recognized and celebrated through employee engagement programs and recognition awards.
If you're seeking a dynamic and collaborative work environment where you can see the direct impact of your performance and thrive both personally and professionally, then the Tech \& Shopping Division is the place for you. Are you ready to own the strategy for our next\-generation platform?
### Compensation Range
Ziff Davis provides a range for the base pay. Factors that may be used to determine your actual pay may include your specific job related knowledge, skills, experience, and geographic location. The salary compensation for this role is $185,000 to $200,000\. Individual pay within the compensation range for this business unit specific role is determined based on a variety of factors including experience, scope of the role, capabilities to perform the role, education and training, as well as business and company performance.
Ziff Davis is an Equal Opportunity Employer. At Ziff Davis, Diversity, Equity, and Inclusion (DEI) has always been about fairness, equal opportunity, and belonging. DEI enables us to attract and retain the best talent, regardless of background or circumstances, while enabling our thousands of employees worldwide to thrive. “Doing Is Greater Than Talking” is the call to action that unites our diversity, equity, and inclusion efforts across Ziff Davis. By bringing our employees together in community, amplifying their voices, and clarifying our hiring, engagement, education, and giving efforts, we continue to work toward a diverse workforce where all feel they belong and can learn and build great careers.
Salary Context
This $185K-$200K range is above the median 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 Ziff Davis, 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 ($192K) sits 12% below the category median. Disclosed range: $185K to $200K.
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.
Ziff Davis AI Hiring
Ziff Davis has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $200K - $200K.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% 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 3,708 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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