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About This Role
POS\-32916
Role Summary
Our mission at HubSpot is to help millions of organizations grow better.
As Senior Growth Marketer, AI Connectors, you will define and execute the growth strategy for HubSpot's AI connector portfolio across platforms such as ChatGPT, Gemini, and future AI ecosystems. HubSpot's AI connectors enable users to access and interact with HubSpot data and workflows directly within AI platforms such as ChatGPT and Gemini.
In this role, you'll help shape how HubSpot is surfaced, recommended, and selected as organizations increasingly rely on AI\-powered experiences to evaluate and adopt software. You will work at the intersection of growth marketing, content strategy, AI discovery, and customer adoption, helping organizations discover and realize value from HubSpot through AI\-powered experiences. This role is ideal for a marketer who combines strong acquisition and content instincts with a deep curiosity about AI platforms, emerging discovery channels, and how organizations use AI to evaluate and adopt software.
### What You'll Do
- Develop and execute the growth strategy for HubSpot's AI connector portfolio to drive awareness, adoption, and customer acquisition across AI platforms such as ChatGPT, Gemini, and future connector ecosystems.
- Own activation and onboarding programs that help customers discovered through AI channels realize value quickly and successfully adopt HubSpot.
- Partner with Product Marketing, Product, Engineering, and Partnerships teams to launch new connector capabilities and coordinate go\-to\-market initiatives.
- Develop and scale AEO and content strategies that strengthen HubSpot's authority across AI ecosystems, increasing how frequently and accurately HubSpot is recommended within AI\-generated experiences.
- Identify, test, and scale distribution strategies across web content, affiliate programs, partner ecosystems, and marketplaces to increase HubSpot's visibility and acquisition through AI\-driven discovery.
- Design and execute experiments that improve discoverability, engagement, and conversion across AI\-driven acquisition channels.
- Establish scalable measurement frameworks for AI\-native acquisition channels and define how success is measured across emerging distribution surfaces.
- Monitor competitor activity, platform changes, AI ecosystem trends, and evolving connector experiences to identify new growth opportunities and help position HubSpot as a leader in AI\-powered customer acquisition.
### What You'll Bring
#### Required Qualifications
- 5\+ years of experience in growth marketing, content strategy, demand generation, SEO, AEO, or related acquisition\-focused marketing roles.
- Proven experience building and executing growth programs that drive measurable customer acquisition, activation, or revenue outcomes.
- Strong understanding of SEO, Answer Engine Optimization (AEO), and AI search.
- Experience developing content strategies that improve discoverability, authority, and customer acquisition.
- Strong familiarity with AI platforms such as ChatGPT, Claude, Gemini, or similar technologies, and an understanding of how users discover and interact with products within those ecosystems.
- Experience marketing products that rely on ecosystem, marketplace, integration, or partner\-driven distribution models.
- Experience working cross\-functionally with Product, Engineering, Partnerships, and Marketing teams to launch and scale growth initiatives.
- Strong analytical skills and the ability to translate performance data into strategic recommendations and experimentation plans.
- Excellent communication and storytelling skills, with the ability to influence stakeholders across multiple teams.
- Ability to thrive in ambiguity and build scalable programs where established playbooks do not yet exist.
#### Nice\-to\-Have Qualifications
- Experience with product\-led growth (PLG) motions tied to organic acquisition channels.
- Experience running AI discoverability, entity optimization, or structured content experiments.
- Experience marketing products within AI, marketplace, ecosystem, or integration\-driven businesses.
- Experience leveraging AI tools and platforms to develop, optimize, or scale content and acquisition programs.
### Where You'll Work
- Location: United States
- Work location preference eligibility: Remote
- Travel/Shift: No regular travel requirements identified at this time.
*We know the**confidence gap* *and* *impostor syndrome* *can get in the way of meeting spectacular candidates, so please don't hesitate to apply — we'd love to hear from you.*
*If you need accommodations or assistance due to a disability, please reach out to us* *using this form**.*
*At HubSpot, we value both flexibility and connection. Whether you're a Remote employee or work from the Office, we want you to start your journey here by building strong connections with your team and peers. If you are joining our Engineering team, you will be required to attend a regional HubSpot office for in\-person onboarding. If you join our broader Product team, you'll also attend other in\-person events, such as your Product Group Summit and other gatherings, to continue building on those connections.*
*If you require an accommodation due to travel limitations or other reasons, please inform your recruiter during the hiring process. We are committed to supporting candidates who may need alternative arrangements*
*Massachusetts Applicants:* *It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.*
*Germany Applicants:* *(m/f/d) \- link to HubSpot's Career Diversity page* *here**.*
*India* *Applicants:* *link to HubSpot India's equal opportunity policy* *here**.*
About HubSpot
HubSpot (NYSE: HUBS) is an AI\-powered customer platform with all the software, integrations, and resources customers need to connect marketing, sales, and service. HubSpot's connected platform enables businesses to grow faster by focusing on what matters most: customers.
At HubSpot, bold is our baseline. Our employees around the globe move fast, stay customer\-obsessed, and win together. Our culture is grounded in four commitments: Solve for the Customer, Be Bold, Learn Fast, Align, Adapt \& Go!, and Deliver with HEART. These commitments shape how we work, lead, and grow.
We're building a company where people can do their best work. We focus on brilliant work, not badge swipes. By combining clarity, ownership, and trust, we create space for big thinking and meaningful progress. And we know that when our employees grow, our customers do too.
Recognized globally for our award\-winning culture by Comparably, Glassdoor, Fortune, and more, HubSpot is headquartered in Cambridge, MA, with employees and offices around the world.
Explore more:
- *HubSpot Careers*
- *Life at HubSpot on Instagram*
*HubSpot may use AI to help screen or assess candidates, but all hiring decisions are always human. More information can be found* *here**. By submitting your application, you agree that HubSpot may collect your personal data for recruiting, global organization planning, and related purposes. We may use CLEAR ID Verification during the hiring process to confirm your identity and help maintain a safe, secure, and trusted experience for all candidates. Refer to HubSpot's* *Recruiting Privacy Notice* *for details on data processing and your rights.*
Salary Context
This $94K-$142K range is in the lower quartile 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 HubSpot, 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($118K) sits 46% below the category median. Disclosed range: $94K to $142K.
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.
HubSpot AI Hiring
HubSpot has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $138K - $142K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 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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