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About This Role
At CloudFactory, we are a mission-driven team passionate about unlocking the potential of AI to transform the world. By combining advanced technology with a global network of talented people, we make unusable data usable, driving real-world impact at scale.
More than just a workplace, we’re a global community founded on strong relationships and the belief that meaningful work transforms lives. Our commitment to earning, learning, and serving fuels everything we do as we strive to connect one million people to meaningful work and build leaders worth following.
Our Culture
At CloudFactory, we believe in building a workplace where everyone feels empowered, valued, and inspired to bring their authentic selves to work. We are:
- Mission-Driven: We focus on creating economic and social impact.
- People-Centric: We care deeply about our team’s growth, well-being, and sense of belonging.
- Innovative: We embrace change and find better ways to do things together.
- Globally Connected: We foster collaboration between diverse cultures and perspectives.
If you’re passionate about innovation, collaboration, and making a real impact, we’d love to have you on board!
Role Summary
CloudFactory is seeking a technically fluent, analytically rigorous AI Marketing Operations Manager to own the systems, data, and campaign infrastructure that power our global pipeline. This is a builder's role at the intersection of demand generation, performance marketing, and applied generative AI.
You will also direct freelance and agency partners and help manage our web development agency, from a technical perspective, during our website rebuild.
You will architect and steward our HubSpot ecosystem, engineer scalable acquisition programs across paid, AI-native, and email channels, and translate performance data into strategic guidance for executive leadership. The ideal candidate thinks in systems, operates with precision, and is energized by the opportunity to redefine how B2B demand is generated in an AI-first market.
Responsibilities:
HubSpot Ecosystem Architecture and Governance* CRM Governance and Data Integrity: Own the health, structure, and integrity of the HubSpot database, including deduplication, property standards, lifecycle stage definitions, and segmentation frameworks that enable precise, account-based targeting.
- Marketing Automation Engineering: Design, test, and continuously optimize sophisticated workflows, lead scoring models, nurture sequences, and lifecycle journeys that accelerate prospects from first engagement to sales readiness.
- Systems Integration: Ensure reliable, bidirectional data flow between HubSpot and the broader go-to-market stack, including advertising platforms, sales engagement tools, and data enrichment services, in close partnership with Sales and Revenue Operations.
Paid Acquisition and AI-Native Channel Strategy* Multi-Channel Paid Media: Plan, launch, and manage B2B acquisition programs across LinkedIn Ads, Google Ads, and emerging AI-native advertising environments such as ChatGPT, with disciplined budget pacing and audience strategy.
- Generative Engine Optimization (GEO): Work with the web development agency to build GEO into the website rebuild, so site structure, content, and markup help Claude, ChatGPT, Gemini, and AI-powered search retrieve and cite CloudFactory content. After launch, run hands-on GEO optimization across the website, LinkedIn, social, and third-party sites, and use those insights to inform paid creative, audience modeling, and spend allocation.
- Conversion Rate Optimization: Design and run structured experiments across ad creative, messaging, offers, and landing pages to systematically reduce cost per acquisition and improve conversion to qualified pipeline.
- Generative AI Content Production: Use Claude, OpenAI, and adjacent generative tools to produce high-quality campaign assets, including social imagery, short-form video, product demonstrations, and 3D visualizations, in alignment with CloudFactory brand standards.
Email Programs and Lifecycle Marketing* Outbound Programs: Build and scale outbound email programs that protect sender reputation, maximize deliverability, and generate meaningful engagement with target accounts.
- Inbound Nurture: Develop compelling, persona-specific nurture sequences that educate buyers and advance marketing qualified leads (MQLs) toward sales qualified opportunities.
Analytics, Attribution, and Executive Insight* Closed-Loop Reporting: Build and maintain HubSpot dashboards that provide a trusted view of ROI, multi-touch attribution, funnel velocity, and pipeline contribution by channel and campaign.
- Strategic Advisory: Deliver monthly performance analyses that inform leadership decisions on budget allocation, channel mix, and strategic direction.
Partner and Website Oversight
- Agency and Freelance Partner Management: Direct freelance and agency partners across paid media, content, and GEO work. Write briefs, set success measures, and review output against agreed quality standards.
- Website Rebuild Support: Help manage our web development agency from a technical perspective during the website rebuild, including requirements, tracking and measurement, search and GEO specifications, quality assurance, and launch readiness.
Requirements
- + HubSpot Expertise: Advanced, hands-on proficiency with HubSpot Marketing Hub and Sales Hub, including workflows, custom properties, lead scoring, and reporting. HubSpot certification preferred. + Paid Media Management: Demonstrated experience managing B2B campaigns within LinkedIn Campaign Manager, Google Ads, and AI-driven advertising platforms. + Email Infrastructure: Strong command of deliverability protocols (SPF, DKIM, and DMARC) and experience with outbound sales engagement platforms. + Web and Tracking: Working knowledge of HTML and CSS for landing page refinement, and experience deploying tracking pixels and conversion events through Google Tag Manager. + Analytical Rigor: A track record of turning complex campaign data into clear, decision-ready recommendations. + Agency Management: Experience directing external agencies or freelancers, with clear briefs, defined success measures, and quality review of their work.
Preferred
- + Experience marketing to enterprise buyers in AI, data, or technology services. + Familiarity with generative engine optimization and how LLMs surface and cite brand content, or a strong appetite to learn it hands-on. + Demonstrated fluency with generative AI tools for creative production and marketing workflow automation. + Experience helping manage a website build, redesign, or migration from a technical perspective.
Benefits
- Great Mission and Culture
- Meaningful Work
- Market competitive salary
- Quarterly variable compensation
- Hybrid Working Model
- Comprehensive medical cover
- Group life insurance
- Personal development and growth opportunities
At CloudFactory, we believe that work should be more than just a job, it should be a platform for growth, impact, and community. Here, you’ll earn with purpose, learn every day, and serve a mission that truly matters. If you're looking for a career where you can develop professionally, contribute meaningfully, and be part of a global movement, we’d love to have you on this journey!
Join us today and be part of our mission to connect people and technology for a better world! Apply now and bring your whole, authentic self to work. We can’t wait to meet you!
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At CloudFactory, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
CloudFactory AI Hiring
CloudFactory has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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