Staff Technologist-AI Architect

$263K - $341K Santa Clara, CA, US Senior AI Architect

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Skills & Technologies

KubernetesLlamaRagTransformers

About This Role

AI job market dashboard showing open roles by category

Staff Technologist-AI Architect

Infrastructure Solutions Group (ISG) builds the products that power infrastructure, solutions, and data management our customers need most. Our teams design and develop the hardware and software that connect infrastructure, accelerate computational workloads, integrate across the stack, protect data continuity, and deliver the platforms, applications, and diagnostics our customers rely on every day at enterprise scale.

Some product builders follow the rules. Ours rewrite them.

Within ISG, innovation isn't just about creating new technology- it's about challenging assumptions and reimagining how work gets done. Engineers define intent, author precise specifications, and orchestrate AI agents that execute at speed and scale. AI is embedded throughout our development process, helping us move faster, learn quicker, and deliver greater impact. Yet the most important contribution remains uniquely human: deciding what to build, designing how it should work, and applying the judgment needed to earn our customers' trust. When your products power critical infrastructure around the world, that responsibility matters.

We move quickly toward the work that matters most. We reward experimentation, encourage bold thinking, and remove unnecessary barriers so great ideas can become reality faster. Our teams embrace a customer-first, first-to-market mindset, transforming rapid feedback into better products and better outcomes. We aren't looking for people who are content with the status quo- we look for builders who question it, improve it, and occasionally rewrite it.

If you're energized by solving complex systems challenges, excited to work alongside AI to amplify your impact, and motivated by the opportunity to shape what's next instead of simply maintaining what's been done before, you'll find your place here.

From applied research to advanced engineering, the Engineering Technologist team has the expertise to

shape ground-breaking products, material and processes. It’s a fascinating field of work. We’re involved in

assessing the competition, developing technology and product strategies and generating intellectual

property. We lead technology investigations, analyze industry capabilities and recommend potential

acquisitions or vendor partner opportunities. Our insights influence product architecture and definitions.

And we work with colleagues across the business to ensure our products always lead the way.

Join us as a Staff Technologist-AI Architect on our ISG-CTOteam in Santa Clara, California to do the best work of your career and make a profound social impact.

What you’ll achieve

As a Staff Technologist-AI Architect, you will architect Dell’s next-generation AI Architecture (including the Dell AI Factory) by driving the empirical characterization, performance modeling, and full-stack integration of frontier AI models and autonomous agentic systems. You will evaluate cutting-edge model architectures, build automated performance benchmarking harnesses across diverse accelerators, and design turnkey production architectures bridging Kubernetes, low-latency inference runtimes, enterprise data platforms, and confidential hardware fabrics.

You will:

  • Architect Dell’s Next-Gen AI Architectures: Drive end-to-end reference architectures and system-level validation for enterprise-scale AI platforms (e.g., Dell AI Factory, rack-scale IR7000/IR9100 liquid-cooled systems, and modular HGX/NVLink clusters).
  • Frontier Model Characterization & Trend Tracking: Track, evaluate, and characterize emerging frontier model architectures and reasoning paradigms (e.g., dense Transformers, large-scale Mixture-of-Experts like DeepSeek-V3/V4 and Llama 4 Scout, hybrid attention, and native multimodal models).
  • Hands-On Deployment & Performance Benchmarking: Design and automate hands-on benchmarking pipelines to rigorously measure critical inference metrics across real-world workloads (e.g., profiling Time to First Token [TTFT], Inter-Token Latency [ITL], prefill/decode disaggregation, KV-cache memory offload, and FP8/FP4 quantization).
  • Agentic Frameworks & Multi-Agent Systems: Understand and design for autonomous, multi-step agentic execution patterns, tool calling, and execution loops (e.g.NVIDIA OpenShell sandboxes).
  • Data & Context Layer for AI: Integrate high-throughput data pipelines and context-grounding platforms to eliminate ETL and prevent hallucinations (e.g., Dell AI Data Platform [AIDP], Starburst/Trino federated SQL, Apache Spark, Elasticsearch vector indexing, and RAG architectures).

Take the first step towards your dream career

Every Dell Technologies team member brings something unique to the table. Here’s what we are looking for with this role:

Essential Requirements

  • 15+ years of professional experience with a Bachelor’s degree or equivalent experience in Engineering (Advanced degree highly preferred)
  • Deep technical understanding of GPU architectures and emerging alternate accelerators to match model workloads to optimal hardware.
  • Experience with Production Storage & High-Speed Data Fabrics: Architect high-throughput, low-latency data and network fabrics essential for continuous AI pipelines (e.g., Dell PowerScale all-flash NFS with GPUDirect Storage [GDS], Dell ObjectScale S3, RoCEv2/Spectrum-X Ethernet, and InfiniBand).
  • Experience designing Confidential Computing & Enterprise Security: Implement hardware-anchored zero-trust security and data-in-use protection for sensitive enterprise models and regulated data (e.g., NVIDIA Confidential Computing, AMD SEV-SNP, Intel TDX, and SPIFFE/mTLS attestation).
  • Inference Serving Engines & Ecosystem Mastery: Deploy, optimize, and evaluate production-grade inference serving frameworks (e.g., vLLM, TensorRT-LLM, SGLang, and Triton Inference Server).
  • Enterprise Kubernetes & Infrastructure Orchestration: Architect resilient containerized AI environments utilizing modern Kubernetes platforms, operators, and schedulers (e.g., Red Hat OpenShift, Kubernetes Gateway API, NVIDIA GPU Operator, and Run:ai).

Desirable Requirements

  • Ability to travel and to assess customer requirements, develop blueprint based on the customer's geographical area with strong verbal and written communication skills along with customer and partner project/program leadership experience

Salary Context

This $263K-$341K range is above the 75th percentile for AI Architect roles in our dataset (median: $175K across 37 roles with salary data).

Role Details

Title Staff Technologist-AI Architect
Location Santa Clara, CA, US
Category AI Architect
Experience Senior
Salary $263K - $341K
Remote No

About This Role

This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.

The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.

Across the 9,079 AI roles we're tracking, AI Architect positions make up 1% of the market. At Dell Technologies, this role fits into their broader AI and engineering organization.

AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.

What the Work Looks Like

Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.

AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.

Skills Required

Kubernetes (4% of roles) Llama Rag (7% of roles) Transformers (1% of roles)

Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.

Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.

Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.

Compensation Benchmarks

AI Architect roles pay a median of $224,070 based on 236 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($302K) sits 35% above the category median. Disclosed range: $263K to $341K.

Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.

Dell Technologies AI Hiring

Dell Technologies has 11 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Architect. Positions span Seattle, WA, US, Hopkinton, MA, US, San Francisco, CA, US. Compensation range: $158K - $476K.

Location Context

Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).

Career Path

Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.

From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.

Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.

What to Expect in Interviews

AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.

When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.

AI Hiring Overview

The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.

The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).

AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.

The AI Job Market Today

The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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

Based on 236 roles with disclosed compensation, the median salary for AI Architect positions is $224,070. Actual compensation varies by seniority, location, and company stage.
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
About 6% of the 9,079 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Dell Technologies is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI Architect positions include Senior Engineer, AI Architect, Engineering Manager, Principal Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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