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About This Role
Overview
We are seeking a hands\-on Global Head of Marketing \& AI Innovation to build and lead a modern, AI\-enabled marketing function. In this role, you will transform how marketing operates across content, design, campaigns, reporting, and workflows by embedding AI into day\-to\-day execution. You will own the global marketing strategy across brand, digital, website strategy, and client and expert engagement, ensuring alignment with company growth priorities.
The ideal candidate will lead a global team to build a more efficient and data\-driven marketing operation—accelerating content production, improving campaign performance, and unlocking better insights to support faster, smarter decision\-making.
This is an opportunity for a strong marketing leader who is highly comfortable leveraging AI to drive execution at scale. You will take ownership, lead cross\-functional initiatives, and build a more agile, scalable, and high\-performing AI\-enabled marketing organization that delivers measurable business impact.
This is a hybrid position. We are seeking candidates who are either based in New York City or willing to relocate.
What You'll Own:
AI \& Automation Strategy
- Define and execute the AI and automation roadmap across the marketing and revenue technology stack
- Identify, prioritize, and deliver scalable AI\-driven marketing use cases aligned to business goals and operational efficiency
- Translate business needs into AI\-enabled solutions in partnership with data, engineering, and revenue teams
- Lead end\-to\-end execution of AI initiatives from concept through delivery, including defining requirements, success metrics, and governance
- Partner closely with sales and senior stakeholders to integrate AI\-enabled processes and communicate strategy, progress, and impact through executive\-level presentations
Marketing Execution
- Lead the global marketing function and a team of 15\+ while developing a modern, digital\-first marketing operation across content creation, reporting, and marketing operations
- Coordinate with internal stakeholders to ensure marketing projects move forward efficiently and deadlines are met
- Drive AI\-enabled transformation across marketing workflows to increase speed, scale, and efficiency
- Develop and execute integrated marketing campaigns that support demand generation, client acquisition, retention, and revenue growth across key markets and segments
- Manage the global marketing budget, external agencies, vendors, and tools to ensure resources are allocated effectively, and marketing investments deliver measurable impact
Brand Strategy
- Refine and evolve the company's brand positioning, messaging, and market narrative to ensure consistency across client, expert, employee, and candidate audiences
- Partner with client\-facing teams to create marketing materials and campaigns that drive client engagement and revenue growth
- Leverage AI\-driven insights to inform positioning, segmentation, and messaging strategy
- Support employer branding initiatives by developing content and messaging that strengthen the company's reputation, culture, and talent attraction efforts
- Own website strategy, content, SEO, conversion optimization, and overall digital performance to improve engagement, lead capture, and brand visibility
Reporting \& Measurement
- Define and report on success metrics for AI and automation initiatives
- Serve as a strategic partner to the executive leadership team, providing marketing insights, performance reporting, and recommendations that support company priorities
- Leverage dashboards and reporting to measure impact across pipeline, conversion rates, efficiency, and revenue
What You Have:
- 12\+ years of related experience across marketing, program management, or product ownership roles within a complex organization
- Significant AI expertise with a proven track record of delivering AI\-driven capabilities and measurable business impact
- Strong B2B marketing experience in professional services, information services, SaaS, consulting, financial services, or another relationship\-driven business
- Proven ability to apply AI and automation to increase efficiency, improve marketing productivity, strengthen business outcomes, and support revenue growth
- Strong analytical mindset with experience using data to evaluate marketing performance, optimize campaigns, and communicate impact to senior leadership
- Fluency with AI and creative tools such as ChatGPT, Claude, Canva AI, Adobe AI/Creative Suite, or similar platforms to enhance go\-to\-market execution
- Experience with marketing automation, CRM systems, campaign management platforms, and reporting tools (e.g., HubSpot, Salesforce, Marketo, Qualified, Outreach, Looker, Tableau)
- Excellent leadership, communication, and change\-management skills, with the ability to influence global functions and regions
- Experience managing marketing budgets, vendors, agencies, and technology investments
What We Offer:
The annual base salary range for this position is $200,000 to $275,000. Additionally, this position is eligible for an annual discretionary bonus based on performance.
You will also be eligible for the following benefits:
- 15 PTO days, 10 legal holidays, and sick days
- Comprehensive medical, dental, and vision plans
- Will match up to 10% of employee contribution for 401(k), life insurance, paid time\-off and parental leave plans
- Commuter benefits and a corporate gym rate
- Development opportunities through the LinkedIn Learning platform
- Free snacks and beverages in the office
- Friday happy hour and "Summer Fridays"
- Year\-round corporate athletic league
- Casual work environment, team building, and other social events
About Guidepoint:
Guidepoint is a leading research enablement platform designed to advance understanding and empower our clients' decision\-making process. Powered by innovative technology, real\-time data, and hard\-to\-source expertise, we help our clients to turn answers into action.
Backed by a network of nearly 1\.75 million experts and Guidepoint's 1,600 employees worldwide, we inform leading organizations' research by delivering on\-demand intelligence and research on request. With Guidepoint, companies and investors can better navigate the abundance of information available today, making it both more useful and more powerful.
At Guidepoint, our success relies on the diversity of our employees, advisors, and client base, which allows us to create connections that offer a wealth of perspectives. We are committed to upholding policies that contribute to an equitable and welcoming environment for our community, regardless of background, identity, or experience.
\#LI\-SG1
\#LI\-Hybrid
Salary Context
This $200K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Guidepoint, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($237K) sits 31% above the category median. Disclosed range: $200K to $275K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Guidepoint AI Hiring
Guidepoint has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $275K - $275K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 2,168 tracked positions. That's 26% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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