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Vice President, AI Talent Aquisition
Date Posted: Jul 8, 2026
Requisition ID: 2034
Location:Fremont, CA, US, 94538
*At Penguin Solutions (Nasdaq: PENG) – The AI Factory Platform Company – we’re building a team of innovators who thrive on collaboration, creativity, and the opportunity to help shape the future of AI. As part of the AI technology revolution, our teams design, build, deploy, and manage AI factories for enterprises, sovereign AI initiatives, and neocloud providers worldwide.*
*Headquartered in Silicon Valley, California, Penguin Solutions operates globally through a network of R\&D, manufacturing, and sales locations. For nearly three decades, we have operated at the intersection of memory and AI/HPC infrastructure. That engineering expertise positions us to power the next generation of AI workloads, from training to inference and agentic AI at scale.*
*Penguin Solutions brings together differentiated infrastructure software, advanced memory, compute systems, end\-to\-end services, and industry\-leading partner solutions in a full\-stack AI factory platform designed to help customers deploy and scale AI workloads with speed and precision.*
*At Penguin Solutions, we value ideas over hierarchy and empower employees to take ownership, drive innovation, and grow through challenging work, continuous learning, and exposure to advanced AI tools and technologies. With flexibility where it matters and a strong focus on outcomes, Penguin Solutions is a place to do your best work, grow your career, and make a meaningful impact.*
Job Overview
The Global VP, AI Talent Acquisition sets the global strategy, standards, and governance for end\-to\-end talent acquisition across regions, while leading execution for senior and executive hiring, including building differentiated approaches to attract and hire AI\-focused talent. The role drives consistent processes, systems, and reporting to improve quality of hire, speed, cost, and candidate experience.
Responsibilities
1\) Strategy, Governance \& Operating Model
- Define and evolve the global TA strategy aligned to enterprise talent priorities and workforce plans.
- Partner with business and technical leaders to translate AI strategy into hiring priorities, role definitions, and workforce plans (e.g., applied ML, GenAI, MLOps, data engineering, AI product).
- Own global TA standards, policies, and end\-to\-end processes; clarify global vs. regional roles and decision rights.
- Serve as the escalation point for governance to ensure consistency, transparency, and compliance across regions.
2\) Senior \& Executive Hiring
- Lead end\-to\-end hiring for Director\-level and executive roles in partnership with senior leaders and HR.
- Build and maintain strategic talent pipelines for critical roles and capabilities; provide market insight to inform hiring decisions.
- Develop sourcing and assessment strategies for AI/ML and data roles, including calibrated interview frameworks, technical evaluation partners, and competitive offer/closing approaches in tight talent markets.
- Ensure consistent assessment and selection standards for senior hiring.
3\) Metrics, Reporting \& Continuous Improvement
- Own the global TA KPI framework (quality, speed, cost, and candidate experience) and define reporting standards.
- Consolidate regional performance into monthly dashboards; lead executive reviews with CHRO and senior leadership.
- Use insights to identify risks and drive improvement actions across processes, capability, and partner performance.
4\) Systems, Data \& Tools
- Set the global TA systems strategy (e.g., ATS) including selection, implementation governance, and data standards.
- Ensure consistent system adoption, data integrity, and reporting across regions.
- Leverage data, automation, and responsible AI\-enabled recruiting capabilities (where appropriate) to improve sourcing efficiency, screening quality, and pipeline visibility for critical skill segments.
5\) Programs \& Onboarding
- Design and govern global recruiting program frameworks (e.g., intern/trainee/MBA and other early\-career programs).
- Define success metrics, roles, and governance for programs and onboarding.
- Lead the design and global implementation of a consistent onboarding framework in partnership with HR, IT, and business leaders.
6\) Partners, Employer Brand \& Enablement
- Set the global strategy and governance for executive search firms, agencies, and external partners; manage preferred supplier models and performance reviews.
- Partner with Communications/Marketing to align employer brand/EVP and candidate experience standards; build strategic talent pipeline approaches.
- Shape EVP messaging and targeted campaigns to attract AI/ML and data talent (e.g., thought leadership, community engagement, university/research relationships, and conference presence).
- Enable stakeholders through training for hiring managers and leaders (process, interviewing/assessment, decision quality).
Qualifications
- 13\+ years of progressive talent acquisition leadership, including global or multi\-region scope; experience leading senior/executive hiring, including scarce technical talent segments (e.g., AI/ML, data, software engineering).
- Demonstrated experience sourcing, assessing, and closing AI/ML and data talent across levels (individual contributor through senior leadership), with strong understanding of AI labor markets, compensation dynamics, and candidate motivations.
- Demonstrated ability to build governance, operating models, and scalable TA processes across geographies.
- Strong analytics and executive communication skills; able to translate TA metrics into actions and decisions.
- Experience with ATS strategy, data standards, and driving adoption and change management.
- Collaborative, consultative leader with strong stakeholder management and vendor/partner oversight experience.
Location
This role is located in Fremont, CA and is expected to be onsite three days per week.
Travel
As needed.
Compensation \& Benefits
The base pay range that the Company reasonably expects to pay for this position in California is $240,000 \- $270,000; the pay ultimately offered may vary based on business considerations, including job\-related knowledge, skills, experience, and education. The position is bonus\-eligible, and there are medical, dental, and vision benefits available. There is a 401k saving plan and other benefits, such as Paid Time Off, Life Insurance, and an Employee Assistance Plan.
Inclusion \& Belonging Statement
We are committed to creating an inclusive environment that embraces differences and fosters belonging for all.
Equal Opportunity Statement
We are an Affirmative Action/Equal Opportunity Employer and strongly committed to all policies which will afford equal opportunity employment to all qualified persons without regard to age, national origin, race, ethnicity, creed, gender, disability, veteran status, or any other characteristic protected by law.
Salary Context
This $240K-$270K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Penguin Solutions, 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 $218,750 based on 3,817 positions with disclosed compensation. This role's midpoint ($255K) sits 17% above the category median. Disclosed range: $240K to $270K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Penguin Solutions AI Hiring
Penguin Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Fremont, CA, US. Compensation range: $270K - $270K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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