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Job Title: Director / VP, AI Infrastructure Product Strategy
Location: San Jose, CA or Irvine, CA
Compensation: $275K - $325K base plus equity
Requirements: AI Infrastructure, Product Strategy, Technical Product Marketing, Hyperscalers, Data Center Architecture, AI Accelerators / GPU Systems, Memory Systems (DDR, HBM, PCIe/CXL), Technology Roadmap
Company Brief
We are a leading provider of high-performance modular memory subsystems and next-generation storage-class memory solutions. We specialize in cutting-edge technologies that enable innovation in AI, cloud computing, and enterprise infrastructure. Our product portfolio includes DDR4/DDR5 DIMMs, NAND-based SSDs, and advanced interconnect technologies such as CXL and PCIe.
Role This is a strategic, highly visible role responsible for identifying important and commercially meaningful problems in AI infrastructure and translating those problems into product opportunities. The successful candidate will develop close relationships with architects, engineers, technology executives, and business leaders at hyperscalers, cloud service providers, server OEMs, and other companies deploying AI infrastructure.
You will investigate how AI workloads are evolving, understand the infrastructure challenges created by those workloads, and identify opportunities where our company can create differentiated products.
You will work closely with our Chief AI Systems Architect, AI Hardware / Memory Systems Architects, AI Inference / Systems Software Architects, engineering, and executive teams to evaluate market opportunities and translate them into product concepts and a roadmap.
Key Responsibilities
Customer & Market Insights
- Build relationships with hyperscalers, cloud providers, OEMs, and enterprise data center leaders.
- Conduct customer discovery to identify AI infrastructure challenges, requirements, and emerging needs.
- Maintain market intelligence on customer pain points, trends, and competing solutions.
AI Infrastructure Strategy
- Stay current on AI infrastructure technologies, including LLM inference, agentic AI, GPUs, HBM, DDR, CXL, AI networking, KV-cache, edge AI, and data center architectures.
- Monitor industry developments and identify future market opportunities.
Product Strategy & Opportunity Development
- Translate customer needs and technology trends into product opportunities.
- Define product concepts, value propositions, target customers, and use cases.
- Assess technical feasibility, market potential, and strategic fit.
Product Definition & Validation
- Develop customer-backed product requirements and specifications.
- Quantify business and technical value.
- Validate concepts with prospective customers before major R&D investment.
Roadmap & Business Planning
- Own the AI product and technology roadmap.
- Prioritize opportunities based on customer impact, differentiation, feasibility, and ROI.
- Present recommendations and business cases to executive leadership.
Cross-Functional Leadership
- Partner with architecture, engineering, sales, business development, and executive teams to guide product development and commercialization.
Competitive & Strategic Analysis
- Track competitors, emerging technologies, and market gaps.
- Evaluate partnership, investment, licensing, and acquisition opportunities.
- Support strategic diligence and maintain a technology watchlist.
Qualifications
- Bachelor's degree in Engineering, Computer Science, or related field; MBA or advanced degree preferred.
- 10+ years in semiconductors, AI infrastructure, data centers, computing systems, product management, or product strategy.
- Experience working with hyperscalers, cloud providers, OEMs, enterprise infrastructure customers, or semiconductor companies.
- Strong understanding of AI infrastructure and data center architectures.
- Proven ability to translate customer needs into successful products.
- Strong communication, analytical, and cross-functional leadership skills.
Preferred Experience
- AI/ML infrastructure, LLMs, generative AI, GPUs, AI accelerators
- HBM, DDR, CXL, PCIe/UCIe, memory pooling/disaggregation
- AI networking, storage, KV-cache infrastructure, PIM/near-memory computing
- AI inference optimization, edge AI, data center architecture
- Technology strategy, strategic partnerships, venture investing, or M&A
#Enghp
Benefits
- Vacation/PTO
- Medical
- Dental
- Vision
- Equity
- 401k w/ match
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Mike Vandenbergh - Lead Recruiter
For this position, you must be currently authorized to work in the United States without the need for sponsorship for a non-immigrant visa. This is a new role.
CyberCoders will consider for Employment in the City of Los Angeles qualified Applicants with Criminal Histories in a manner consistent with the requirements of the Los Angeles Fair Chance Initiative for Hiring (Ban the Box) Ordinance.
This job was first posted by CyberCoders on 10/15/2026 and applications will be accepted on an ongoing basis until the position is filled or closed.
Everforth CyberCoders is proud to be an Equal Opportunity Employer All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, sexual orientation, gender identity or expression, national origin, ancestry, citizenship, genetic information, registered domestic partner status, marital status, status as a crime victim, disability, protected veteran status, or any other characteristic protected by law. Our hiring process includes AI screening for keywords and minimum qualifications, and a virtual recruiter as part of the application process. A human recruiter reviews all results. Click here for details on our virtual recruiter . Everforth CyberCoders will consider qualified applicants with criminal histories in a manner consistent with the requirements of applicable state and local law, including but not limited to the Los Angeles County Fair Chance Ordinance, the San Francisco Fair Chance Ordinance, and the California Fair Chance Act. Everforth CyberCoders is committed to working with and providing reasonable accommodation to individuals with physical and mental disabilities. Individuals needing special assistance or an accommodation while seeking employment can contact a member of our Human Resources team at [email protected] to make arrangements.
Copyright © 2026 Everforth, Inc. All rights reserved.
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 CyberCoders, 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.
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.
CyberCoders AI Hiring
CyberCoders has 7 open AI roles right now. They're hiring across AI/ML Engineer. Positions span White Plains, NY, New York, NY, Pittsburgh, PA.
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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