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About This Role
Principal Technical Product Marketing Manager, AI Solutions
Description \-
HP is seeking a Principal Technical Product Marketing Manager to help define, position, and accelerate adoption of its AI portfolio, including HP ZGX AI Stations and Agentic AI solutions .
This role is ideal for someone who combines deep technical expertise with strong product marketing fundamentals. You should be equally comfortable discussing model architectures, inference optimization, agent frameworks, open\-source AI ecosystems, and developer workflows with engineers as you are translating those capabilities into compelling business value for customers, partners, and executive stakeholders.
You will serve as the bridge between Product Management, Engineering, Sales, and Customers, helping shape product strategy, drive successful launches, and establish HP as a leader in the rapidly evolving AI market. Success in this role requires hands\-on experience with AI development workflows, coding, open\-source models, and enterprise AI deployments, along with the ability to convert highly technical differentiation into measurable business outcomes.
What You'll Own
Product Positioning \& Messaging
- Develop differentiated positioning, messaging, and value propositions for HP ZGX AI Stations, Agentic AI solutions, and related AI offerings.
- Translate complex technical capabilities into clear, compelling customer value for developers, AI practitioners, IT leaders, and executive buyers.
- Build solution narratives around local AI development, AI inference, agentic workflows, enterprise AI adoption, and hybrid AI deployments.
- Develop audience\-specific messaging for developers, data scientists, IT decision makers, business leaders, and channel partners.
Go\-to\-Market Strategy \& Execution
- Define and execute go\-to\-market strategies for new AI solutions, platforms, and category\-defining innovations.
- Partner with Product Management and Engineering to bring AI solutions from concept through launch and broad market adoption.
- Develop launch plans, readiness programs, customer\-facing content, and market activation strategies that drive pipeline growth and customer adoption.
- Identify target industries, use cases, buyer personas, and sales motions to accelerate market penetration.
Technical Leadership \& Customer Engagement
- Serve as a trusted technical advisor to customers, partners, sales teams, and internal stakeholders on AI Stations, AI development environments, inference workloads, and Agentic AI solutions.
- Deliver compelling customer presentations, executive briefings, technical workshops, demonstrations, and architecture discussions tailored to both business and technical audiences.
- Lead customer conversations on AI architectures, open\-source model adoption, AI development workflows, inference optimization, and agentic AI deployment strategies.
- Provide technical guidance and best\-practice recommendations to customers evaluating AI solutions and deploying AI applications at scale.
- Support strategic customer engagements, proof\-of\-concept initiatives, benchmark reviews, and technical validation efforts.
- Partner with Product Management and Engineering teams to influence roadmap priorities based on customer and market feedback.
- Mentor junior team members and contribute to the technical development of peers through coaching, knowledge sharing, and best\-practice guidance.
- Develop and deliver technical training programs for sales teams, partners, customers, and internal stakeholders to increase technical readiness and accelerate adoption.
- Act as a subject matter expert on Generative AI, Agentic AI, open\-source models, inference, model serving, and enterprise AI deployment patterns.
Technical Marketing \& Thought Leadership
- Create technical content including white papers, solution briefs, architecture guides, benchmark summaries, competitive analyses, customer presentations, blogs, webinars, and demos.
- Develop use cases and solution narratives demonstrating how AI Stations and Agentic AI platforms enable organizations to build, deploy, and scale AI applications and agents locally and within enterprise environments.
- Represent HP at industry events, customer briefings, webinars, workshops, partner engagements, and technical conferences.
- Establish HP's point of view on emerging AI trends, enterprise AI adoption, and agentic AI technologies.
Customer \& Market Insights
- Engage directly with AI developers, data scientists, machine learning engineers, solution architects, and enterprise customers to understand market needs and technical requirements.
- Lead Voice of Customer initiatives and gather competitive intelligence to influence product strategy and roadmap priorities.
- Monitor trends across Agentic AI, AI infrastructure, foundation models, inference technologies, and enterprise AI adoption.
- Develop actionable market insights that help shape positioning, product direction, and go\-to\-market priorities.
Sales \& Partner Enablement
- Build technical enablement assets including sales presentations, battlecards, competitive comparisons, ROI frameworks, reference architectures, demos, and solution playbooks.
- Enable sales teams, channel partners, system integrators, and alliance partners to effectively position and sell HP AI solutions.
- Support strategic customer engagements, proof\-of\-concept initiatives, technical evaluations, and partner programs.
- Help field teams articulate HP's differentiated value in AI development, inference, and agentic workflows.
Cross\-Functional Leadership
- Partner closely with Product Management, Engineering, Business Development, Sales, and Marketing teams to ensure alignment on product strategy and go\-to\-market priorities.
- Influence stakeholders across a highly matrixed organization to drive execution and business outcomes.
- Act as the voice of the market while ensuring technical credibility with internal and external audiences.
Required Qualifications
- 8 \+ years of experience in Product Marketing, Product Management, Solutions Marketing, Developer Marketing, Solutions Architecture, AI Consulting, AI Engineering, or a related technical discipline.
- Coding experience with Python , SQL, JSON, and YAML
- Familiarity with GPU/CPU concepts, Windows and Linux environments, Docker, Git, and repeatable experiment practices.
- Practical experience building, testing, deploying, or evaluating AI applications, copilots, agents, or machine learning solutions.
- Experience working with open\-source foundation models such as Llama, Mistral, DeepSeek, Gemma, Qwen, or similar technologies.
- Experience with AI development frameworks and tools such as LangChain , LangGraph , Semantic Kernel, Hugging Face, Ollama , vLLM , OpenAI APIs, or comparable platforms.
- Expert level understanding of:
Generative AI
Agentic AI
Retrieval\-Augmented Generation (RAG)
Multi\-Agent Systems
Prompt Engineering
Model Fine\-Tuning and Optimization
AI Inference and Model Serving
Model Evaluation and Deployment
AI Orchestration Frameworks
- Strong analytical and data\-driven decision\-making skills.
- Knowledge of modern AI development workflows, developer tools, and enterprise AI deployment patterns.
- Demonstrated ability to present complex technical concepts to customers, executives, developers, and engineering audiences.
- Experience delivering technical workshops, customer briefings, enablement sessions, or conference presentations.
- Ability to provide architectural guidance and consult with customers on AI solution design, model selection, deployment strategies, and inference optimization.
- Proven track record launching and driving adoption of complex B2B technology solutions.
- Excellent verbal, written, and presentation skills across both technical and executive audiences.
Education
- Bachelor's degree in Computer Science , Computer Engineering, Software Engineering, Data Science, Artificial Intelligence, or a related technical field, or equivalent practical experience.
- Advanced technical degree preferred.
The pay range for this role is $147,050\.00 \- $216,900\.00 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.
Job \-
Software
Schedule \-
Full time
Shift \-
No shift premium (United States of America)
Travel \-
25%
Relocation \-
Yes
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 $147K-$216K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($181K) sits 15% below the category median. Disclosed range: $147K to $216K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
HP AI Hiring
HP has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Spring, TX, US, San Francisco, CA, US, Palo Alto, CA, US. Compensation range: $175K - $230K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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