Interested in this AI/ML Engineer role at Extreme Networks?
Apply Now →About This Role
Over 50,000 customers globally trust our end\-to\-end, cloud\-driven networking solutions. They rely on our top\-rated services and support to accelerate their digital transformation efforts and deliver unprecedented progress. With double\-digit growth year over year, no provider is better positioned to deliver scalable outcomes than Extreme.
Inclusion is one of our core values and in our DNA. We are committed to fostering an inclusive workplace that embraces our differences and creates an atmosphere where all our employees thrive because of their differences, not in spite of them.
Become part of Something big with Extreme! As a global networking leader, learn why there’s no better time to join the Extreme team. Job Title: Marketing AI and Innovation Leader
Department: Marketing
Reports to: Chief Marketing Officer (CMO)
Role Summary:
The Marketing AI Transformation Leader will serve as the driving force behind the adoption and scaling of AI technologies across the marketing organization. This individual will serve as a bridge between senior leadership, marketing teams, and cross\-functional stakeholders, ensuring AI tools and strategies align with the company's broader business objectives. By identifying high\-impact use cases, fostering a culture of AI literacy, and championing responsible AI practices, this leader will position the company to achieve operational excellence and innovation in marketing.
In addition to leading adoption efforts, this role will establish and chair a cross\-functional Marketing AI Council. The Council will act as a guiding coalition, bringing together key stakeholders from all functions of marketing, as well as representatives from IT and legal to ensure the responsible, strategic, and cohesive implementation of AI within marketing.
Key Responsibilities:
- Strategic Use Case Development:
- Collaborate with CMOs, VPs, and team leads to identify and prioritize AI\-driven marketing use cases that deliver measurable business value.
Pilot initiatives that demonstrate the potential of generative AI in improving efficiency, personalization, and ROI across marketing campaigns.
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- AI Council Leadership:
- Establish and lead a Marketing AI Council composed of cross\-functional leaders and contributors to guide the marketing team’s AI strategy.
- Ensure the Council operates as a hub for ideation, oversight, and alignment on AI adoption efforts, shared learning, facilitating discussions, and documenting outcomes.
- Develop AI governance frameworks and usage guidelines in collaboration with the Council to address ethical, operational, and security considerations as it relates to marketing usage.
Represent marketing’s best interest on the corporate IT\-led AI Council and share best practices and learnings across the organization
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- AI Literacy and Enablement:
- Lead the development of AI literacy programs, including training for marketers at all levels on tools, ethical considerations, and best practices.
Work with the Council to establish and communicate AI usage guidelines across the organization.
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- Change Management:
- Drive cultural and behavioral change by inspiring teams to embrace AI as a tool for creativity and efficiency.
Create a robust communication plan to share wins, lessons learned, and insights from pilot projects.
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- Leadership and Influence:
- Act as an influencer and thought leader, working with cross\-functional teams and stakeholders to build buy\-in for AI adoption.
Foster relationships with key executives and managers to overcome resistance and address concerns.
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- Performance Measurement and Reporting:
- Develop and track KPIs to measure the impact of AI tools on marketing performance, such as cost savings, time\-to\-market, and content quality.
- Regularly present updates and insights to the CMO and other senior leaders.
Qualifications:
- Experience:
- + 10\+ years in marketing, with experience across multiple functions such as content creation, demand generation, analytics, and brand strategy.
At least 5 years in a management or leadership role with a focus on change management and cross\-functional collaboration.
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- Technical Skills:
- + Familiarity with AI tools like generative AI platforms, marketing automation systems, and data analytics tools.
Understanding of the AI adoption curve and best practices for scaling AI in large organizations
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- Leadership Skills:
- + Proven ability to lead initiatives without direct authority by influencing across organizational levels and functions.
Experience in founding or leading councils, task forces, or similar collaborative bodies to drive strategic initiatives.
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- Educational Background:
- + Bachelor’s degree in marketing, business administration, or a related field (MBA or advanced degree preferred).
Alternate Titles:
- Marketing AI \& Innovation Leader
- Marketing Operations \& AI Strategy Director
- Director, Marketing AI Transformation
- Head of Marketing AI Integration
- AI\-Driven Marketing Strategy Lead
Ideal Candidate Attributes:
- Deep Curiosity and Enthusiasm for AI:
The ideal candidate has a forward\-thinking mindset and passion for exploring how AI can transform marketing’s impact on the business. They stay ahead of industry trends, proactively seek opportunities to apply AI in impactful ways, and inspire their teams to embrace innovation with confidence.
- Exceptional Communication Skills:
With the ability to articulate complex AI concepts in simple, compelling terms, this leader can effectively engage diverse audiences, from technical teams to senior executives. They excel in presenting ideas, facilitating discussions, and securing alignment across stakeholders to ensure a shared vision for AI adoption.
- Strong Organizational Skills:
Managing multiple pilot programs, strategic initiatives, and cross\-functional collaborations requires exceptional organizational capabilities. This individual is adept at prioritizing tasks, allocating resources, and ensuring projects are delivered on time and within scope, even in a dynamic and fast\-paced environment.
- Proven Leadership and Influence:
The candidate leads by example, fostering trust and collaboration across teams and levels of the organization. They thrive in environments where they must influence without direct authority, rallying support for AI initiatives through persuasive storytelling, data\-backed insights, and consistent engagement.
- Strategic and Results\-Oriented:
Balancing a strategic mindset with a focus on execution, this leader excels at identifying high\-impact use cases that align with business objectives. They are committed to delivering measurable outcomes, whether through cost savings, time efficiencies, or improved campaign performance.
- High Adaptability and Resilience:
This individual demonstrates flexibility and resilience. They can navigate ambiguity, pivot when necessary, and stay motivated in the face of challenges, continuously driving the organization toward its transformation goals.
- Commitment to Ethical AI Practices:
A champion of responsible AI, the candidate ensures that AI adoption aligns with ethical guidelines, safeguarding brand reputation and promoting trust. They actively address concerns related to privacy, security, and fairness, fostering an environment of accountability and transparency.
- Collaborative Team Builder:
The ideal leader is skilled at assembling and nurturing cross\-functional teams, such as a Marketing AI Council. They recognize and amplify diverse perspectives, creating a culture where experimentation, innovation, and knowledge\-sharing thrive.
Extreme Networks, Inc. (EXTR) creates effortless networking experiences that enable all of us to advance. We push the boundaries of technology leveraging the powers of machine learning, artificial intelligence, analytics, and automation. Over 50,000 customers globally trust our end\-to\-end, cloud\-driven networking solutions and rely on our top\-rated services and support to accelerate their digital transformation efforts and deliver progress like never before. For more information, visit Extreme's website or follow us on Twitter, LinkedIn, and Facebook. *We encourage people from underrepresented groups to apply. Come Advance with us! In keeping with our values, no employee or applicant will face discrimination/harassment based on: race, color, ancestry, national origin, religion, age, gender, marital domestic partner status, sexual orientation, gender identity, disability status, or veteran status. Above and beyond discrimination/harassment based on “protected categories,” Extreme Networks also strives to prevent other, subtler forms of inappropriate behavior (e.g., stereotyping) from ever gaining a foothold in our organization. Whether blatant or hidden, barriers to success have no place at Extreme Networks.*
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Extreme Networks, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Extreme Networks AI Hiring
Extreme Networks has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Raleigh, NC, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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