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About This Role
Overview
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Mailchimp is a leading marketing platform for small businesses. We empower millions of customers around the world to build their brands and grow their companies with a suite of marketing automation, multichannel campaign, CRM, and analytics tools.
This team, is focused on building the AI and agentic systems that power Mailchimp's next generation of intelligent marketing products — from generative content and campaign optimization to autonomous agents that act on behalf of our customers. You'll work at the intersection of applied AI research and production engineering, turning models and agentic architectures into features that reach millions of small businesses, while helping define how Mailchimp adopts AI across the broader product stack, including integration with Intuit's AI platform.
You will collaborate with teams throughout the company and represent our group in cross\-departmental projects and forward\-looking discussions with Directors and VPs.
At Mailchimp, we value learning, collaboration and clarity. You'll find success here if you enjoy writing code, but you're aware that your job isn't only about shipping: you are a welcoming contributor who knows how to communicate with your team in order to build great software. You're comfortable with the pace of AI development — iterating quickly, running experiments, and translating ambiguous research into reliable production systems. You've broken production environments — and then buckled down, fixed them, and maybe you held a blameless post\-mortem about it!
Responsibilities
- Work cross\-functionally with AI Science, product, and platform teams to identify high\-impact opportunities for AI and agentic features, and set the overall technical direction for building them
- We're an AI\-first team — not only do we use AI tooling to move faster in our daily work, we build the AI products and agentic solutions themselves, designing systems where LLMs and agents are the product
- Design, build, and operate the services, APIs, and infrastructure that power LLM\-based features and autonomous agents at scale
- Partner with product teams to translate AI Science research and models into customer\-facing features — shaping the APIs, orchestration layers, and platform capabilities that product engineers build on
- Design, plan, and lead the integration of AI/ML systems with Intuit's AI platform and Modern SaaS (MSaaS) ecosystem, adopting Intuit's Services Paved Road patterns for AI services
- Build and evolve the data and evaluation foundations — training/eval pipelines, feedback loops, guardrails, and observability — that keep AI features accurate, safe, and performant at scale
- Empower team members by providing technical guidance and mentorship on both software engineering and applied AI practices
- Embrace and demonstrate our values: humility, creativity, and independence
Qualifications
- You have experience designing, building, or shipping AI/ML\-powered products, including LLM\-based features, RAG systems, or autonomous/agentic workflows
- You have experience driving cross\-functional projects and communicating technical concepts — including AI/ML tradeoffs — to both engineering and non\-technical teams
- You have experience with distributed systems, including load management, monitoring, and resiliency, ideally as applied to AI inference or agent workloads
- You have experience with evaluation, guardrails, or responsible AI practices for generative or agentic systems
- You have worked with large\-scale data — building or operating data pipelines, event streaming, or high\-volume data stores — that feed ML/AI systems
- Coding experience in Python, Java, Kotlin, Go, or other languages that can be demonstrated through a portfolio of code
- Familiarity with GraphQL, GCP, Kubernetes, or agent orchestration frameworks (e.g., LangChain, LangGraph) is a plus (but not required — we'll help you get there)
- You are a self starter that can work independently while also putting an emphasis on mentorship of those around you
- You are passionate about building and delivering great AI\-driven features for customers
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Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position may be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit®: Careers \| Benefits). Pay offered is based on factors such as job\-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.
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 4,317 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Intuit, 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 $218,500 based on 729 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Intuit AI Hiring
Intuit has 12 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, Data Scientist, AI Product Manager. Positions span New York, NY, US, Mountain View, CA, US, San Diego, CA, US. Compensation range: $190K - $328K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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.
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