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About This Role
Overview
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At NetApp, we have a history of helping customers turn challenges into business opportunities. As part of our sales organization, your role will require a strategic blend of technical acumen and charismatic client engagement, ensuring that every partnership is nurtured towards it maximum potential. As a pivotal link between NetApp and our clients, your contributions will directly influence the growth and direction of our department, making a lasting impact on the organization's success. This is more than a job—it's a chance to be part of a team that values innovation, supports professional growth, and celebrates shared victories.
Own Every Moment at NetApp
At NetApp, your ideas power innovation. We lead in intelligent data infrastructure—delivering unified storage, integrated data services, and solutions that help organizations unlock the full potential of their data, from AI to multicloud.
Ready to innovate and contribute to our path to $10B? Here, you'll collaborate with passionate teams, tackle real\-world challenges, and see your impact in how customers transform and grow. If you're ready to bring curiosity, creativity, and drive to every moment, NetApp is where your journey begins. Join teams that drive results, innovate to elevate, and excel together across every function.
About the Role:
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AI is changing how go\-to\-market teams operate. We are seeking an AI GTM Transformation Lead to lead the end\-to\-end AI transformation of our Go\-to\-Market (GTM) Revenue Operations organization. This is a highly strategic, senior individual contributor role responsible for shaping how AI is applied across GTM RevOps, turning fragmented ideas into a coherent portfolio of scalable, governed, high\-value transformation initiatives.
This leader will sit across the business and act as the connective tissue between the business, AI technologies, and execution teams. The role focuses on redesigning business outcomes using AI\-native thinking, leveraging AI agents, orchestration layers, automation platforms, and human\-in\-the\-loop systems to fundamentally transform how work gets done.
The role goes beyond automation delivery. It requires someone who can define the outcome design for AI transformation, redesign workflows for human\-agent collaboration, guide initiatives from proof of concept through scaled autonomy, and ensure success is measured not only by deployment, but by adoption, trust, and business value.
What You'll Do:
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Lead AI\-Driven GTM Transformation
- Own the GTM RevOps AI transformation roadmap, aligning AI initiatives to business priorities, operational outcomes, and measurable value.
- Serve as the strategic leader connecting business goals, process design, governance, adoption, and execution.
Build and Prioritize the AI Portfolio
- Identify, assess, and prioritize high\-impact AI, automation, and workflow transformation opportunities.
- Partner with stakeholders to define business requirements, future\-state processes, success metrics, dependencies, and governance needs.
- Prepare initiatives for investment and portfolio review through structured evaluation and prioritization.
Design Human \+ AI Operating Models
- Reimagine workflows to optimize collaboration between people and AI agents.
- Define autonomy levels, exception handling, governance controls, and scalability requirements.
- Drive initiatives from proof\-of\-concept through pilot and enterprise\-scale deployment.
Drive Cross\-Functional Delivery
- Coordinate business, data, platform, and AI delivery teams to execute transformation initiatives.
- Translate business needs into requirements, user stories, acceptance criteria, and operational processes.
- Ensure solutions are scalable, integrated, and aligned with enterprise governance standards.
Shape Governance and Adoption
- Contribute to AI governance forums, prioritization decisions, and operating model design.
- Ensure appropriate attention to risk, security, compliance, and data governance.
- Partner with change leaders to build adoption, enablement, and trust across GTM teams.
Measure Impact and Continuous Improvement
- Establish and track metrics for adoption, productivity, quality, efficiency, and business impact.
- Use operational insights and user feedback to refine solutions, improve agent performance, and evolve the AI roadmap.
- Stay current on emerging AI capabilities and identify opportunities that create practical business value.
What Success Looks Like:
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- AI initiatives are consistently well\-shaped, decision\-ready, and prioritized against the most important GTM business outcomes.
- GTM RevOps realizes measurable improvements in efficiency, cycle time, productivity, and operational capacity through well\-governed AI and agentic automation.
- Human\-agent workflows are implemented with clarity, trust, and strong user adoption rather than confusion or shadow experimentation.
- The organization develops reusable patterns, playbooks, and operating mechanisms that enable AI scale across multiple GTM processes.
- Delivery is coordinated and coherent across teams, with minimal duplication, clear ownership, and strong alignment to enterprise priorities.
- Leaders see this role as a strategic partner who can connect business transformation, AI innovation, and execution discipline in a fast\-moving environment.
Education and Experience:
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- Typically requires a minimum of 15 years of related experience
- Deep experience in Revenue Operations, GTM Operations, Sales Operations, Customer Operations, Solutions Engineering, or a closely related GTM function, with a strong understanding of how go\-to\-market teams operate end to end.
- Proven ability to lead complex, cross\-functional transformation initiatives that require influence across business, technical, and operational stakeholders.
- Strong program and portfolio orchestration skills, including prioritization, dependency management, executive communication, and delivery alignment across multiple initiatives.
- Experience designing workflows, service models, or operating processes that span people, systems, data, and governance.
- Strong understanding of AI\-assisted workflows, copilots, agentic automation, or intelligent process redesign.
- Ability to define value cases, KPIs, and success metrics for transformation programs and hold initiatives accountable to business outcomes.
- Experience working with ambiguity and building new operating models or capabilities where no playbook exists.
- Excellent executive presence and communication skills, with the ability to synthesize complex issues and influence senior leaders.
- Comfort partnering with technical teams and working across APIs, prompts, automation platforms, AI tools, and data/system dependencies, even if not acting as the primary engineer.
- Demonstrated ability to simplify complex concepts and enable broad adoption among non\-technical users.
Compensation:
The target salary range for this position is 196,350 \- 292,600 USD. The salary offered will be determined by the candidate's location, qualifications, experience, and education and may be outside of this range. Final compensation packages are competitive and in line with industry standards, reflecting a variety of factors, and include a comprehensive benefits package. This may cover Health Insurance, Life Insurance, Retirement or Pension Plans, Paid Time Off, various Leave options, Performance\-Based Incentives, employee stock purchase plan, and/or restricted stocks (RSU’s), with all offerings subject to regional variations and governed by local laws, regulations, and company policies. Benefits may vary by country and region, and further details will be provided as part of the recruitment process.
At NetApp, we embrace a hybrid working environment designed to strengthen connection, collaboration, and culture for all employees. This means that most roles will have some level of in\-office and/or in\-person expectations, which will be shared during the recruitment process.
Equal Opportunity Employer:
NetApp is firmly committed to Equal Employment Opportunity (EEO) and to compliance with all federal, state and local laws that prohibit employment discrimination based on age, race, color, gender, sexual orientation, gender identity, national origin, religion, disability or genetic information, pregnancy, protected veteran status, and any other protected classification.
Why You'll Thrive at NetApp
At NetApp, you won't wait for the perfect moment—you'll make it. The early planning, the extra thought, the bold idea that turns good into great: That's how our people operate and how we continue to push the boundaries of data infrastructure.
NetApp is the trusted partner for organizations transforming data into opportunity. As the only enterprise\-grade storage service natively embedded in Google Cloud, AWS, and Microsoft Azure, we empower customers to run everything from traditional workloads to enterprise AI with unmatched performance, resilience, and security.
Our culture
We celebrate mold breakers, bold thinkers, and problem solvers. We reward initiative, impact, and ownership. We provide flexibility so you can balance professional ambition with your personal life. Here, differences are not just welcomed—they drive everything we do.
If you're ready to innovate, rise to the challenge, and own every moment \- make your next move your best one. now.
Submitting an Application
To ensure a streamlined and fair hiring process for all candidates, our team only reviews applications submitted through our company website. This practice allows us to track, assess, and respond to applicants efficiently. Emailing our employees, recruiters, or Human Resources personnel directly will not influence your application.
AI Disclosure
For select roles, some stages of our hiring process may use artificial intelligence tools to help evaluate applications and candidate selection. These tools support—rather than replace—human decision\-making.
Our values
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Put the customer at the center. Care for each other and our communities. Think and act like owners. Build belonging every day. Embrace a growth mindset.
Benefits
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### Volunteer time off
40 hours of paid volunteer time each year.
### Well\-being
Employee Assistance Program, fitness, and mental health resources to help employees be their best.
### Time away
Paid time off for vacation and to recharge.
Salary Context
This $196K-$292K 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 NetApp, 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 Required
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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($244K) sits 12% above the category median. Disclosed range: $196K to $292K.
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.
NetApp AI Hiring
NetApp has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span Remote, US, San Jose, CA, US. Compensation range: $253K - $292K.
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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