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Scan/Imaging AI Technical Program Manager Description -
We are seeking an innovative and results-driven Technical Program Manager to define and deliver next-generation AI-powered solutions that transform document workflows, business processes, and customer experiences. In this role, you will shape the technology roadmap for intelligent imaging and workflow solutions by leveraging Artificial Intelligence (AI), Large Language Models (LLMs), Natural Language Processing (NLP), AI Agents, Computer Vision, and Edge AI to create differentiated solutions for enterprise customers.
As a Technical Program Manager, you will collaborate closely with product management, imaging and software architects, engineering teams, and development partners to lead the prototyping, development, integration, and deployment of scalable AI solutions that deliver measurable business outcomes. You will identify emerging technology opportunities, translate customer and business needs into innovative product capabilities, and accelerate the commercialization of AI-enabled imaging and workflow solutions.
The ideal candidate combines strong program leadership with a solid technical foundation in systems engineering and cross-functional product development. You excel at aligning diverse stakeholders around a common vision, navigating complex technical challenges, and driving execution across multiple disciplines. You are passionate about innovation, thrive in a collaborative environment, and are motivated by delivering transformative solutions that create customer and business value.
Responsibilities
Strategy & Roadmap Leadership
- Own and maintain the Scan/Imaging AI roadmap across Embedded, Edge, and Cloud solution architectures.
- Develop and drive software, platform, and solution roadmaps based on customer requirements, technical capabilities, strategic priorities, and business objectives.
- Lead Strategic Intent (SI) planning activities and investment recommendations for future digital workflow and AI initiatives.
- Evaluate and prioritize emerging opportunities based on customer value, business impact, technical feasibility, and competitive differentiation.
- Drive roadmap topics including Hydra/Fabric Flow, NPU strategy, Guided Redaction 2.0/3.0, Intent First Scanning, Scan-to-Email enhancements, intelligent file naming, document summarization, Job Archive, Data Loss Detection, and future AI workflow solutions.
Program Management & Execution
- Lead portions of the planning and execution of multiple complex technical programs supporting HP's Scan/Imaging AI strategy.
- Run and facilitate the weekly Scan/Imaging AI PMT, roadmap reviews, defect reviews, and strategic planning sessions.
- Partner with cross-functional software, firmware, platform, and solutions teams to prioritize, plan, track, and execute AI initiatives.
- Perform program analysis, manage risks, identify dependencies, and influence required course corrections.
- Lead technical and business SWAT activities focused on high-priority opportunities and issues.
- Manage risks and resolve issues affecting scope, schedule, release readiness, quality, and investment decisions.
- Ensure successful transfer of validated concepts into funded product development programs.
Requirements & Solution Definition
- Define customer requirements, solution requirements, business cases, and product use cases for AI-enabled scan and imaging experiences.
- Partner with stakeholders to establish customer-experience strategies that guide investment and prioritization decisions.
- Develop solution concepts, architecture recommendations, and technical requirements documentation for roadmap initiatives.
- Ensure alignment between customer needs, business goals, and technology investments.
- Drive requirements development for both incremental feature improvements and future AI platform capabilities.
Technical Leadership & Innovation
- Provide technical leadership and guidance to ensure solutions meet quality, performance, scalability, security, and usability objectives.
- Lead evaluations of emerging AI technologies, architectures, models, and deployment strategies.
- Assess trade-offs between embedded, edge, on-premise, and cloud AI solutions.
- Influence architecture decisions supporting reusable AI platforms and long-term solution scalability.
- Encourage innovation and facilitate exploration of new technologies, research initiatives, and industry partnerships.
- Support university research collaborations and advanced technology investigations that contribute to future roadmap opportunities.
Stakeholder & Customer Engagement
- Represent Scan/Imaging AI initiatives across broader HP organizations and strategic business initiatives, including BTG, Precise Scan, Fabric Flow, and future workflow platforms.
- Communicate project status, roadmap updates, investment recommendations, and technical strategies through executive reviews and leadership presentations.
- Conduct customer visits, demonstrations, technology workshops, and innovation lab engagements to identify new customer needs and validate solution concepts.
- Ensure alignment among stakeholders through continuous communication, review cycles, and collaborative decision-making.
- Serve as a key advocate for customer-centric AI solution development.
Process & Continuous Improvement
- Continuously assess and improve the processes used to manage AI solution development and portfolio execution.
- Establish best practices for AI solution definition, roadmap governance, capability prioritization, and investment planning.
- Drive improvements in planning, execution, customer validation, and decision-making processes across the Scan/Imaging AI portfolio.
Education & Experience Recommended
- Four-year or Graduate Degree in related Engineering field, Business Management, or related discipline, or equivalent experience.
- Typically 7–10 years of experience in technical program management, product management, software development, systems engineering, or related technical leadership roles.
- Experience managing cross-functional programs involving software, firmware, cloud services, or platform development preferred.
- Experience with document workflows, scanning, imaging, OCR, workflow automation, or enterprise productivity solutions preferred.
Preferred Certifications
- Program Management Professional (PMP)
- Agile or Scrum Certification
- AI/ML or Cloud Technology Certifications (preferred)
Knowledge & Skills
- Artificial Intelligence & Machine Learning
- Technical Program Management
- Product Management
- Roadmap Development
- Strategic Planning
- Software Development Lifecycle
- Agile Methodology
- Scrum & Kanban
- Requirements Definition
- Solution Architecture
- Risk Management
- JIRA
- Customer Experience Design
- Workflow Automation
- Cloud & Edge Computing
- AI Platform Technologies
- Change Management
- Continuous Improvement
- Executive Communications
- Data-Driven Decision Making
The pay range for this role is $105,050 to $161,800 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.
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 $105K-$161K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($133K) sits 37% below the category median. Disclosed range: $105K to $161K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
HP AI Hiring
HP has 3 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer. Positions span Vancouver, WA, US, Spring, TX, US, Spring, TX. Compensation range: $161K - $230K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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