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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. That’s because we bring new thinking to age\-old problems, like how to use data most effectively in the most efficient possible way. As an Engineer with NetApp, you’ll have the opportunity to work with modern cloud and container orchestration technologies in a production setting. You’ll play an important role in scaling systems sustainably through automation and evolving them by pushing for changes to improve reliability and velocity.
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.
Job Summary
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This role partners with leaders across NetApp to apply AI\-native ways of working to high\-value processes inside the company. The work is hands\-on and engagement\-based: partner with a team to understand how their work runs today, design a future state where AI agents accelerate and augment the work, build and pilot the solution, and partner with the team to make the new way of working durable.
The role applies product management discipline — diagnosis, prioritization, specification, iteration, and measurement — to the design and delivery of agent\-driven workflows. The unit of delivery is a working solution adopted by a partner team, not a roadmap or a recommendation.
The role sits within the Agentic Product Management team under the VP, Analytics Growth and AI Native Engineering, and reports to the Director, Agentic Product Management. The team works in close partnership with platform engineering, security, and compliance to ensure that the workflows we ship are safe, auditable, and scalable.
A few principles shape how the team approaches its work:
- We start from outcomes, not from tasks. The first question on any engagement is what the process is for and what good looks like, not how to automate the current steps.
- We deliver working solutions, not recommendations. Every engagement ends with a prototype that real users can run.
- We design with audit trails. Every workflow is inspectable: which agent did what, against what input, with what reasoning.
- We expand trust deliberately. Agents start with bounded authority, and authority grows as evidence accumulates.
Key Responsibilities
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- Partner with the team that owns the process. Build the relationship with the leader and the operators who do the work today. Conduct working sessions to understand inputs, outputs, decision points, tools, cycle time, and the moments where the work most often gets stuck. Produce a current\-state view that the partner team agrees is accurate.
- Define the outcome. Work with the partner\-team leader to articulate what the process is actually trying to achieve and what good would look like. Identify which steps require human judgment, which serve regulatory or customer\-facing purposes, and which exist because of tooling constraints. Quantify the prize: hours returned, cycle time reduced, quality improved, or capacity unlocked.
- Design the future state. Produce a future\-state design that uses agents to accelerate and augment the work, with humans engaged where their judgment, relationships, or accountability are essential. The design specifies the agents involved, the data they read and write, the tools they use, the decision rights they hold, the escalation paths to humans, and the controls that govern their behavior.
- Build the prototype. Build a working version of the future\-state design, hands\-on, in code. Use Cursor, Claude Code, and the other AI development tools available to the team. The prototype is the artifact that proves the design is real and ready for first users.
- Pilot with first users. Run the prototype with a defined set of first users in the partner organization. Train them, support them, gather structured feedback, and iterate. Document what works, what does not, and what needs to change before the workflow runs at scale.
- Define the governance. Specify the audit trail, the cost controls, the escalation triggers, the access scope, and the trust\-progression criteria that allow the workflow to operate inside NetApp’s security, legal, and compliance posture. Partner with the relevant review teams to get the workflow approved.
- Instrument and measure. Define and capture the metrics that demonstrate the workflow is delivering the intended outcome. Produce the evidence the partner\-team leader needs to defend and expand the new way of working.
- Hand off to platform engineering. When the workflow is validated and the partner team has adopted it, transition the solution to platform engineering with the integration patterns, runbooks, and adoption playbook needed to operate it at NetApp scale.
- Contribute patterns back. Capture reusable components from each engagement — agent designs, prompt patterns, governance templates, integration approaches — and contribute them to the team’s pattern library so future engagements move faster.
Education and Experience
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- Typically requires a minimum of 12 years of related experience with a Bachelor's degree or equivalent professional experience. Demonstrated experience leading end\-to\-end process redesign and transformation engagements, with a track record of moving from diagnosis through implementation rather than stopping at recommendation. This experience may have been gained in:
+ The digital, AI, or engineering arm of a major strategy or consulting firm — examples include BCG X (Gamma, Platinion), McKinsey QuantumBlack or McKinsey Digital, Bain Vector or Advanced Analytics, Deloitte AI \& Engineering, Accenture Applied Intelligence — or a comparable practice
+ An internal AI, digital, or transformation team at a scaled technology company, with ownership of production deployments of agent\-driven or AI\-augmented workflows
+ A founding or early operating role at a startup building agent\-native products
- Hands\-on fluency with current AI tooling, including LLMs, coding assistants, and agent frameworks. Familiarity with Cursor and Claude Code is preferred; experience with other orchestration platforms and agent frameworks is welcome.
- Strong process design, structured thinking, and stakeholder management skills, including the ability to navigate security, legal, and platform engineering review without losing the intent of the original design.
- Comfort operating in environments where the problem is not yet well defined, and where part of the work is to define it.
- Excellent written and verbal communication, with the ability to disagree productively and adjust as evidence evolves.
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.
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: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($244K) sits 37% above the category median. Disclosed range: $196K to $292K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
NetApp AI Hiring
NetApp has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span Remote, US, US. Compensation range: $292K - $396K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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