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Product Marketing Manager, AI Solutions – Higher Education \& Research
Description \-
As a Product Marketing Manager for AI Solutions in Higher Education \& Research, you will support the positioning, go‑to‑market (GTM) strategy, and sales enablement for HP’s AI solutions serving universities, research institutions, and academic labs.
In this role, you will help bring AI solutions to market by translating technical capabilities into clear, compelling messaging for academic audiences—including faculty, researchers, and IT teams. You will contribute to how HP enables AI training, inference, and emerging agentic AI workflows across research, instruction, and student access, where cost efficiency, ease of use, and access to compute are critical.
You will work closely with cross\-functional teams across Product, Sales, and Partnerships to develop content, support launches, and execute GTM programs aligned to higher education and research priorities.
Key Responsibilities
Positioning \& Messaging
- Support development of messaging for HP AI solutions tailored to higher education and research customers
- Translate product capabilities (e.g., GPUs, workstations, AI software, agentic AI frameworks, on\-prem compute) into value for academic use cases
- Assist in creating vertical\-specific content focused on research productivity, teaching outcomes, student access, and emerging AI workflows such as multi\-step and agent\-driven applications
Go\-to\-Market Execution
- Contribute to GTM planning and execution for AI solutions across higher ed and research segments
- Help define target audiences (e.g., faculty, researchers, IT) and support campaign development
- Collaborate with cross\-functional teams to ensure messaging consistency across campaigns and launches
Sales \& Partner Enablement
- Develop and maintain sales materials including presentations, one\-pagers, and solution overviews
- Support creation of enablement content such as playbooks, reference architectures, and competitive comparisons
- Help articulate the value of local and hybrid AI environments for agentic and data\-intensive workloads , including cost control and data ownership
- Work with partners and internal teams to ensure alignment on solution messaging and positioning
Customer \& Market Insights
- Conduct research on higher\-ed and research market trends, including the adoption of AI and emerging agent\-based applications
- Support voice\-of\-customer activities such as interviews and feedback synthesis
- Translate insights into recommendations for messaging and campaign improvements
Launch Support \& Coordination
- Support product and solution launches by coordinating assets, timelines, and stakeholder inputs
- Help ensure readiness across marketing and sales teams through content and communication
- Track performance of GTM activities and capture learnings for future improvements
Qualifications
Required
- Bachelor’s degree in Computer Science, Engineering, or a related technical field
- Internship or project experience in product marketing, marketing, business strategy, or a related field
- Foundational understanding of AI, data science, or technical infrastructure concepts (coursework, projects, or internships welcome), including awareness of emerging trends such as generative AI and agentic AI
- Demonstrated ability to translate technical concepts into clear, audience\-friendly messaging through presentations, writing, or academic work
- Strong analytical and problem\-solving skills, with the ability to structure and communicate insights clearly
- Highly collaborative, with a proactive mindset and ability to learn quickly in a fast\-paced environment
Preferred
- Internship experience in B2B technology, AI, SaaS, or hardware\-related environments
- Hands\-on exposure to AI or data science through coursework, research, or personal projects (e.g., model development, datasets, experimentation, or simple agent\-based workflows)
- Experience creating marketing, technical, or sales\-related content (presentations, reports, solution overviews, etc.)
- Interest in higher education, research environments, or emerging AI technologies
We value candidates who can point to specific projects, coursework, or internship work that demonstrate their ability to translate technical concepts—including emerging areas like generative or agentic AI—into clear, compelling narratives.
Skills
- Product \& Solution Marketing Fundamentals
- Go\-to\-Market Execution
- Technical Storytelling \& Communication
- AI \& Emerging Technologies (incl. Agentic AI Awareness)
- Sales Enablement Support
- Market Research \& Competitive Analysis
- Cross\-Functional Collaboration
- Analytical \& Structured Thinking
The pay range for this role is $93,400 to $143,800 USD annually with additional opportunities for pay in the form of bonus and/or equity (applies to United States of America candidates only). Pay varies by work location, job\-related knowledge, skills, and experience.
Benefits:
HP offers a comprehensive benefits package for this position, including:
- Health insurance
- Dental insurance
- Vision insurance
- Long term/short term disability insurance
- Employee assistance program
- Flexible spending account
- Life insurance
- Generous time off policies, including;
- 4\-12 weeks fully paid parental leave based on tenure
- 11 paid holidays
Additional flexible paid vacation and sick leave ( US benefits overview )
*
The compensation and benefits information is accurate as of the date of this posting. The Company reserves the right to modify this information at any time, with or without notice, subject to applicable law.
Equal Opportunity Employer (EEO) \-
HP, Inc. provides equal employment opportunity to all employees and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, citizenship, sexual orientation, age, disability, or status as a protected veteran, marital status, familial status, physical or mental disability, medical condition, pregnancy, genetic predisposition or carrier status, uniformed service status, political affiliation or any other characteristic protected by applicable national, federal, state, and local law(s).
Please be assured that you will not be subject to any adverse treatment if you choose to disclose the information requested. This information is provided voluntarily. The information obtained will be kept in strict confidence.
For more information, review HP’s EEO Policy or read about your rights as an applicant under the law here: “ Know Your Rights: Workplace Discrimination is Illegal "
Posting Expiration Date: 9/20/26
Job \-
Software
Schedule \-
Full time
Shift \-
No shift premium (United States of America)
Travel \-
25%
Relocation \-
No
Equal Opportunity Employer (EEO) \-
HP, Inc. provides equal employment opportunity to all employees and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, citizenship, sexual orientation, age, disability, or status as a protected veteran, marital status, familial status, physical or mental disability, medical condition, pregnancy, genetic predisposition or carrier status, uniformed service status, political affiliation or any other characteristic protected by applicable national, federal, state, and local law(s).
Please be assured that you will not be subject to any adverse treatment if you choose to disclose the information requested. This information is provided voluntarily. The information obtained will be kept in strict confidence.
For more information, review HP’s EEO Policy or read about your rights as an applicant under the law here: “ Know Your Rights: Workplace Discrimination is Illegal "
Salary Context
This $93K-$143K range is in the lower quartile 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 HP, 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 in Demand for This Role
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 ($118K) sits 35% below the category median. Disclosed range: $93K to $143K.
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.
HP AI Hiring
HP has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Fort Collins, CO, US, Spring, TX, US. Compensation range: $143K - $274K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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