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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.
The Mission: Power the Next Generation of AI
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We are standing at an inflection point. AI is transforming every industry, but beneath every breakthrough model lies a critical foundation that few see and even fewer master: the data infrastructure that makes intelligent systems possible.
These positions require 3 days in office per week in one of the following offices: Boulder, CO or Pittsburgh, PA or Raleigh, NC or San Jose, CA.
The Office of the Chief Platform and Technology Officer is assembling an elite team of AI Infrastructure Engineers to build the future of intelligent data systems. We don't just store data—we architect the substrate that powers AI factories, from GPU clusters running training workloads to real\-time inference pipelines serving billions of requests.
This is not a maintenance role. This is a creation role. You will design the systems that enable enterprises to deploy AI at an unprecedented scale, leveraging NetApp's new AI Data Engine (AIDE) and AFX disaggregated storage architecture.
The Opportunity: Shape what comes next and oin us to solve challenges that exist at the absolute frontier of computer science.
- Build AI\-Native Infrastructure Lead the architecture of next\-generation storage systems optimized for AI workloads. Design high\-performance data pipelines for massive\-scale model training, implement intelligent caching for KV stores, and optimize data planes for GPU clusters. Your work will directly accelerate how quickly organizations can move from data to deployed intelligence.
- Pioneer Forward\-Looking Research Work at the intersection of distributed systems, AI, and storage. Investigate novel approaches to scalable AI inferencing systems, semantic data discovery, and data curation systems. Turn proof\-of\-concepts into production systems that redefine industry standards.
- Amplify Your Impact with AI We don't just build AI—we use it. Leverage Cursor, Claude Code, and emerging AI development tools to accelerate your workflow, automate repetitive tasks, and focus on solving problems that matter. You'll operate at the cutting edge of "vibe coding" while maintaining the rigor of production\-grade engineering.
What You'll Bring
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You are a generalist with depth—someone who moves fluidly between AI technologies and distributed system architectures, who gets energy from ambiguous problems, and who possesses what we call "fearless curiosity".
You thrive when:
- Ownership is total: You own problems end\-to\-end, from architecture to production operations, and take pride in systems that run flawlessly at scale
- Change is constant: You view rapid technological shifts as opportunities to add structure and clarity, not as obstacles
- Learning is relentless: You have a rich life of "side quests"—self\-driven projects that demonstrate your curiosity beyond your day job.
- AI is a multiplier: You view AI coding assistants and generative tools as force multipliers that let you tackle complexity that was previously impossible
You bring:
- Deep systems expertise: Mastery of Golang, Python, and C/C\+\+, with an intuitive understanding of file systems, advanced data structures, and algorithms. You understand that storage isn't just about bits on disk—it's about enabling the next wave of intelligent applications
- AI infrastructure fluency: Deep knowledge of AI Infra: Kubernetes, operating systems, Storage systems and distributed systems.
- Quantitative intuition: The ability to build simplified performance models, identify bottlenecks through deep analysis, and design for scalability from first principles
- Growth mindset: For yourself, your team, and the organization. You balance optimism with realism, make smart bets, and understand that some experiments will fail fast while others will drive the company forward
What You'll Create
- AI Data Architectures: Design storage and networking systems that connect structured and unstructured data to LLMs with unprecedented performance, enabling real\-time inference and massive\-scale training
- Intelligent Storage Systems: Build the next generation of ONTAP capabilities, focusing on AI\-specific optimizations like vector store integration, semantic search, and automated data curation
- High\-Performance Infrastructure: Develop systems capable of TB/s throughput and EB\-scale data management, supporting the world's most demanding AI factories
- AI\-Augmented Engineering: Pioneer internal tooling and workflows that use AI to accelerate development, from automated code review to intelligent debugging systems
- Cross\-Functional Impact: Partner with hardware engineers, product managers, and researchers to deliver groundbreaking intelligent storage solutions that power everything from autonomous vehicles to pharmaceutical discovery
Your Qualifications
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Technical Mastery:
- 8\+ years of software development experience with a focus on systems, infrastructure, or storage technologies
- Expert\-level proficiency in Golang, Python, and C/C\+\+
- Deep understanding of Linux kernel development, file systems, and distributed systems
- Experience with performance analysis, optimization techniques, and building quantitative models
- Familiarity with AI/ML infrastructure concepts: GPU computing, model serving, data pipelines, vector databases
Mindset \& Approach:
- Bold Ideas, Grounded Execution: You dream big but ship with precision
- Relentless Curiosity: An insatiable desire to understand how things work and how to make them better
- Collaborative Excellence: You elevate everyone around you through mentorship, knowledge sharing, and constructive feedback
- AI Fluency: Confidence in using AI tools to accelerate all aspects of your work
Preferred Experience:
- Building or optimizing storage systems for AI/ML workloads
- Working with high\-performance computing (HPC) environments or GPU clusters
- Knowledge of network protocols, RDMA, and high\-speed interconnects
- Experience with agile methodologies and rapid prototyping
Education:
- Bachelor's or master's degree in computer science, Engineering, or equivalent experience. We value what you've built and what you know over credentials alone.
*This is a pipeline requisition used to recruit for multiple openings across Levels 3, 4, and 5\. Candidates will be evaluated and assigned to the appropriate level based on their qualifications, skills, and years of relevant experience.*
Compensation:
The target salary range for this position is 130,900 \- 194,700 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 $130K-$194K range is below the median for AI Software Engineer roles in our dataset (median: $189K across 518 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 26,159 AI roles we're tracking, AI Software Engineer positions make up 2% of the market. At NetApp, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $235,100 based on 665 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($162K) sits 31% below the category median. Disclosed range: $130K to $194K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
NetApp AI Hiring
NetApp has 1 open AI role right now. They're hiring across AI Software Engineer. Based in San Jose, CA, US. Compensation range: $194K - $194K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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