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About This Role
Software Senior Principal Engineer – Autonomous AI Software Engineering
Software engineering is being reshaped by autonomous AI agents that can plan, write, test, and review code with increasing independence. Our team works at the forefront of that shift, bringing agent-driven autonomy into the software development lifecycle so that engineers set direction and make key decisions while agents handle execution.
Because the agent landscape evolves rapidly, we stay hands-on: exploring promising approaches, validating them through rigorous experimentation on real engineering workloads, and turning successful outcomes into enterprise-grade software development practices. We are a small, AI-native team operating with startup agility—emphasizing high autonomy, rapid iteration, and seamless human-AI collaboration.
Join us to do the best work of your career and make a profound impact as a Software Senior Principal Engineer on our AI Governance Engineering team in Hopkinton, Massachusetts. This is an onsite role.
What You'll Achieve
You will serve as the technical leader and architect for autonomous AI across our software development lifecycle. You will define the target architecture for agent-driven engineering, lead objective evaluations of commercial, open-source, and in-house technologies, and translate proof-of-concept results into actionable technical strategies for executive leadership.
This is a hands-on architecture role: you will write code, build critical prototypes and integrations directly, and guide engineering teams by technical example from pilot stages to enterprise-scale deployment.
You Will:
- Define Reference Architectures: Architect autonomous agent systems within the SDLC, encompassing orchestration, multi-agent design patterns, tool use, Model Context Protocol (MCP), sandboxed runtime environments, model routing, human-in-the-loop controls, and integration with existing engineering platforms and governance frameworks
- Lead Technical Evaluations: Maintain deep visibility into the agent ecosystem, establish objective evaluation criteria (capability, autonomy, quality, security, scalability, extensibility, and total cost of ownership), and design proof-of-concept tests to drive build-versus-buy decisions
- Own Verification and Validation: Establish the testing, qualification, and acceptance standards for agent-generated code and artifacts, including continuous benchmarking and regression monitoring for agent platforms and underlying models
- Architect for Trust and Safety: Design frameworks ensuring auditability, traceability of agent actions, reproducibility, data privacy, failure containment, robust permission models, and mitigations against vulnerabilities such as prompt injection and unauthorized tool execution
- Drive Platform Adoption: Establish the autonomy maturity roadmap, directly construct core platform capabilities and integrations, mentor senior technical talent, and align technical initiatives across cross-functional engineering organizations
Technologies You'll Work With:
- Agents & LLMs: Commercial and open-source autonomous coding agents/frameworks, frontier and open-weight models, LLM gateways, function/tool calling, Retrieval-Augmented Generation (RAG), and the Model Context Protocol (MCP)
- Engineering & Integration: Python, TypeScript / Node.js, REST and GraphQL APIs, Git-based workflows, and modern CI/CD automation
- Verification & Testing: Automated testing frameworks, Test-Driven Development (TDD), mutation and property-based testing, static code and security analysis, and LLM evaluation benchmarks
- Runtime & Observability: Sandboxed container execution (Docker, Kubernetes), OpenTelemetry tracing, operational metrics/dashboards, and PostgreSQL
Essential Requirements:
- 12+ years of software engineering experience, including 5+ years as an architect or technical lead for large-scale distributed platforms
- Proven track record architecting, deploying, and maintaining production-grade autonomous agent platforms (including agent runtimes, orchestration engines, tool/MCP integrations, context management, and dynamic model routing)
- Demonstrated experience designing multi-agent architectures for autonomous software development (e.g., task decomposition, planner/executor/reviewer paradigms, structured agent handoffs, and human-in-the-loop validation delivering production pull requests)
- Deep technical expertise in secure agent execution environments: sandboxing, workload scheduling, persistent state management, distributed tracing/replay, cost optimization, fine-grained access control, and defense-in-depth against prompt injection and sandbox escape
- Direct experience building evaluation frameworks, automated benchmarking suites, and regression testing pipelines for agents, prompts, and model upgrades, backed by comprehensive knowledge of the open-source and commercial agent ecosystem
Desirable Requirements:
- Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
- Experience scaling an agent platform from prototype to widespread enterprise adoption, or prior experience building commercial agent-based software products
- Advanced familiarity with long-horizon agent reasoning techniques, including autonomous planning, reflection, self-correction, and reinforcement learning from feedback
- Recognized technical leadership in agentic AI, demonstrated through open-source contributions, technical publications, patents, or benchmark releases
About Dell Technologies — ISG Developer Experience
Dell's Infrastructure Solutions Group is a global leader in Solutions, Storage, Compute, and Networking. The Developer Experience organization within ISG is responsible for developer productivity, tooling, observability, and platform engineering for approximately 9,000 engineers. Our current mandate spans agentic SDLC transformation, AI tooling governance, and the operating model changes required to make AI-assisted development the norm — not the exception.
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.
Join us and help build the future of enterprise technology.
Compensation
Dell is committed to fair and equitable compensation practices. The salary range for this position is $200,000 to $275,000
Benefits and Perks of Working at Dell Technologies
Your life. Your health. Supported by your benefits. You can explore the overall benefits experience that awaits you as a Dell Technologies team member — right now at MyWellatDell.com
Who We Are
We believe that each of us has the power to make an impact. That’s why we put our team members at the center of everything we do. If you’re looking for an opportunity to grow your career with some of the best minds and most advanced tech in the industry, we’re looking for you.
Dell Technologies is a unique family of businesses that helps individuals and organizations transform how they work, live and play. Join us to build a future that works for everyone because Progress Takes All of Us.
Dell Technologies is committed to the principle of equal employment opportunity for all employees and to providing employees with a work environment free of discrimination and harassment. Read the full Equal Employment Opportunity Policy.
Visit our Culture Code page to learn more about how we work and lead.
Salary Context
This $200K-$275K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $192K across 198 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 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Dell Technologies, 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 $220,000 based on 1,601 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($237K) sits 8% above the category median. Disclosed range: $200K to $275K.
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 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 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 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 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
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