Interested in this AI Product Manager role at 宏碁股份有限公司?
Apply Now →About This Role
Date: Oct 8, 2026
Location: San Jose, US, San Jose
Company: Acer Group
Job Summary
Acer is seeking an experienced Product Manager to lead product strategy, roadmap planning, and lifecycle management for commercial AI PCs, workstations, and server platforms. This role translates enterprise and education customer needs into competitive hardware solutions, aligning product plans with Intel, AMD, and NVIDIA platform roadmaps and coordinating execution with engineering, supply chain, and local manufacturing teams. The Product Manager connects technical platform capabilities with commercial opportunities, working with Sales, Field Application Engineers, Product Marketing, and channel partners to bring solutions to market. The portfolio supports business and education IT environments, including local AI inference, generative AI, edge computing, and high-performance workloads.
Job Responsibility
- Product strategy and roadmap planning: Own the multi-year commercial product roadmap and lifecycle, from requirements and business case development through launch, refresh, and end of life. Prioritize platforms and configurations based on customer needs, market opportunity, technical feasibility, and business objectives.
- Silicon and platform alignment: Evaluate Intel, AMD, and NVIDIA architectures, release cycles, and platform roadmaps. Align product timing and specifications with processor, GPU, memory, storage, power, and cooling requirements for targeted workloads.
- New product introduction and local manufacturing: Partner with engineering, procurement, supply chain, and local manufacturing or assembly teams to execute new product introduction (NPI). Coordinate build schedules, configuration readiness, component availability, regulatory compliance, and production readiness; track yield and quality issues with the responsible teams.
- Market analysis and product economics: Conduct competitive benchmarking and assess demand across commercial AI PCs, workstations, and servers. Develop business cases and product positioning, manage bill of materials (BOM) cost targets, and recommend pricing and configurations that balance performance, cost, and profitability.
- Commercial channels and customer solutions: Work with Sales, distributors, value-added resellers, and system integrators to understand buying requirements and channel opportunities. Translate enterprise and education IT needs into product requirements, including deployment, manageability, security, reliability, and lifecycle support.
- ISV and ecosystem collaboration: Engage independent software vendors (ISVs) and technology partners to coordinate platform certification, workload validation, and performance testing. Use findings to guide platform decisions and substantiate customer value.
- Go-to-market execution: Partner with Sales, Field Application Engineers (FAEs), and Product Marketing on launch planning, channel enablement, technical whitepapers, competitive battle cards, and customer presentations. Ensure product claims reflect validated capabilities and relevant use cases.
Requirements
- Bachelor’s degree in Computer Science, Electrical Engineering, Industrial Engineering, Business, or a related field, or equivalent practical experience.
- 10+ years of technical product management experience in IT hardware, OEM/ODM, workstations, or servers, with demonstrated ownership of product roadmaps and lifecycle execution.
- Strong understanding of Intel, AMD, and NVIDIA platforms, motherboard architectures, power and cooling constraints, and GPU or accelerator integration.
- Hands-on experience working with local manufacturing or assembly teams, NPI processes, supply chain coordination, and regional production workflows.
- Understanding of commercial channels and enterprise or education IT solutions, with the ability to translate customer and partner requirements into product plans.
- Working knowledge of AI workloads such as local large language model (LLM) inference, generative AI, edge deployment, and workstation-based fine-tuning, and their implications for hardware configuration.
- Ability to develop business cases, analyze competitive offerings and BOM costs, and communicate product priorities and tradeoffs across technical and commercial teams.
- Strong communication and presentation skills, with the ability to explain technical capabilities and business value to customers, partners, and internal stakeholders.
- Experience collaborating with ISVs on hardware certification, AI workload validation, and performance tuning.
Acer is an EEO employer that considers applicants for all positions without regard to race, color, creed, religion, gender, age, national origin, marital status, status with regard to public assistance, disability, veteran status or preferred orientation.
»
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 9,079 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At 宏碁股份有限公司, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 947 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
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.
宏碁股份有限公司 AI Hiring
宏碁股份有限公司 has 1 open AI role right now. They're hiring across AI Product Manager. Based in San Jose, CA, US.
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 Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
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).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.