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Principal Product Operations & Strategy Manager, Edge & AI Solutions Description -
Role Overview
The Principal Product Operations & Strategy Manager strengthens how HP’s Edge & AI product portfolio operates by improving alignment, product practices, and evidence-based decision making across solutions.
Working across Product Management, Engineering, Design, Marketing, and Sales, this role leads the portfolio operating cadence, maintains shared planning and discovery frameworks, and synthesizes insights that inform product strategy. The role helps teams translate strategy into coordinated priorities, understand dependencies, and evaluate outcomes. Individual product managers retain ownership of their products and roadmaps.
Success requires systems thinking, sound judgment in ambiguous situations, and the ability to turn customer, market, product, and deployment evidence into clear recommendations for product leaders.
Key responsibilities:
Product Alignment & Portfolio Coherence
- Facilitate portfolio planning and alignment discussions that connect Edge & AI strategy to shared priorities, sequencing, and success criteria.
- Identify gaps, overlaps, and dependencies across hardware, platform, and solution work, and bring risks and trade-offs to product leaders for resolution.
Maintain a consolidated view of product plans to help teams coordinate decisions and understand how individual offerings contribute to the portfolio. *
Product Operating Practices, Cadence & Standards
- Lead quarterly planning, portfolio reviews, and cross-team prioritization discussions, including preparation of inputs and documentation of decisions and follow-up actions.
- Develop and maintain shared product practices for planning, customer validation, readiness reviews, and learning from product outcomes.
- Keep operating practices lightweight and practical, improving clarity and coordination as the portfolio grows.
Support teams in adopting shared discovery, prioritization, and outcome-tracking approaches, adapting them to each solution’s maturity and needs.
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Outcome Based Product Strategy Support
- Work with product teams and data owners to identify relevant product, customer, business, and deployment measures and bring them into portfolio reviews and planning.
- Support structured customer and user learning by providing shared approaches to interviews, surveys, and other research activities.
Consolidate findings from customer research, product usage, field deployments, and partner feedback into insights and recommendations that inform portfolio decisions.
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Core Outputs & Artifacts
- Consolidated Portfolio Roadmap: A maintained view of product roadmaps that shows priorities, dependencies, and sequencing across Edge & AI offerings. Individual product managers remain responsible for their product roadmaps.
- Shared Product Discovery Framework: Practical guidance for problem definition, customer validation, and solution testing, adapted to each solution’s maturity.
- Synthesized Customer and Deployment Insights: Consolidated findings from customer interviews, field deployments, usage data, and partner feedback, with implications for portfolio planning and product decisions.
What Success Looks Like
- Product teams have a shared understanding of portfolio priorities, dependencies, sequencing, and success measures.
- Portfolio reviews and planning decisions consistently use customer, deployment, usage, and business evidence.
- Teams use practical product operating practices that improve coordination and decision making across solutions.
Product leaders have timely visibility into portfolio plans, customer insights, and product performance, with clear recommendations on risks and trade-offs. *
Experience knowledge & skills
- 8+ years of relevant experience in Product Management, Product Operations, or portfolio management, including experience working with teams delivering software and hardware solutions.
- Demonstrated ability to operate at the portfolio level, influencing product direction without direct ownership of individual products.
- Demonstrated experience establishing or improving product planning cadences, portfolio reviews, or shared product operating practices.
- Ability to facilitate trade-off discussions and gain adoption of shared practices across teams without direct authority.
- Strong qualitative synthesis skills—connecting customer insights, deployment experiences, and product signals into clear narratives.
- Experience preparing executive‑ready portfolio reviews, recommendations, and trade‑off discussions.
- Comfort working with product, engineering, sales, and field teams to source inputs and reconcile conflicting signals.
- Working knowledge of product and business metrics, including the ability to interpret data, identify gaps, and translate findings into recommendations. This role partners with data owners and does not require ownership of analytics infrastructure.
Excellent communication skills, including the ability to work effectively with senior product leaders. *
Education:
- Bachelor’s degree in Computer Science, Engineering, Data Science, Business, or a related field, or equivalent relevant professional experience.
MBA is a plus but not required *
Disclaimer
- This job description describes the general nature and level of work performed in this role. It is not intended to be an exhaustive list of all duties, skills, responsibilities, knowledge, etc. These may be subject to change and additional functions may be assigned as needed by management.
Salary
The pay range for this role is $147,050 to $230,850 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 -
Relocation -
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-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2094 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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% 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 $212,160 based on 13,809 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($188K) sits 11% below the category median. Disclosed range: $147K to $230K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
HP AI Hiring
HP has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, MLOps Engineer. Positions span Austin, TX, Palo Alto, CA, US, Fort Collins, CO, US. Compensation range: $161K - $230K.
Location Context
Across all AI roles, 6% (665 positions) offer remote work, while 10,097 require on-site attendance. Top AI hiring metros: New York (3,262 roles, $221,000 median); San Francisco (2,767 roles, $260,600 median); Los Angeles (1,585 roles, $210,000 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, 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 $266,050 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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