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About This Role
Overview
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Overview
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Come join Intuit's Consumer Group as a Manager 3, Software Engineering, helping power TurboTax — the \#1 tax prep platform serving tens of millions of US consumers each year. The Consumer Group is reinventing the tax experience with AI, GenAI, and an expert\-in\-the\-product model that combines world\-class software with virtual tax experts. As an engineering manager, you will lead a team of full\-stack and platform engineers building the next generation of TurboTax — AI\-assisted, mobile\-first, and personalized for every customer's financial life.
Responsibilities
Responsibilities
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- Lead, coach, and grow a high\-performing team of software engineers building customer\-facing TurboTax experiences and/or the AI/platform capabilities that power them
- Drive end\-to\-end ownership of major TurboTax surfaces (web, mobile, services, AI agents) with simple, elegant, durable, scalable architecture
- Partner with Product, Design, Data Science, AI/ML, and Tax Content to deliver measurable improvements in conversion, completion, customer benefit, and NPS
- Bring thought leadership in AI/GenAI, agentic systems, and personalization to disrupt how consumers do their taxes
- Drive operational and engineering excellence — uptime during tax season peak load, quality, speed\-to\-market, developer productivity, and data\-driven decision making
- Define and uphold engineering standards across the Consumer Group — code quality, CI/CD, observability, SRE practices, and incident response
- Coach and mentor engineers on modern architecture, AI\-assisted development, and career growth; build a strong inclusive team culture
- Plan and run agile delivery — quarterly OKRs, sprint planning, backlog grooming, and clear stakeholder communication
- Operate effectively during the high\-intensity US tax season (Jan–Apr), with strong incident and release management discipline
Qualifications
Qualifications
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- BS/MS in Computer Science or equivalent work experience
- 8\+ years of software development experience, including 2\+ years managing software engineering teams
- Experience building consumer\-scale SaaS or mobile applications (consumer fintech, tax, or e\-commerce a strong plus)
- 3\+ years of cloud experience building and operating services on AWS; experience with Lambda, container orchestration, and serverless is a plus
- Strong full\-stack background: Java microservices, Spring Boot, Python, React/React Native, GraphQL/REST APIs
- Working knowledge of AI/ML and GenAI — LLMs, RAG, agents, evals — and how to apply them to customer experiences responsibly
- Strong understanding of SOA, distributed systems, and event\-driven architectures
- Experience working with Product Management, Design, and Data Science partners to ship customer\-facing features
- Knowledge of agile methodologies (Scrum/Kanban), CI/CD pipelines, observability, and SRE practices
- Track record of building diverse, inclusive teams and developing engineers' careers
- Excellent communication skills — able to translate complex technical decisions for both technical and non\-technical audiences
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. The expected base pay range for this position is:
Bay Area California $ 244,000\- 330,000
Southern California $ 237,500\- 321,500
Salary Context
This $237K-$330K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $190K across 193 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 3,824 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 $234,620 based on 682 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($283K) sits 21% above the category median. Disclosed range: $237K to $330K.
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.
Intuit AI Hiring
Intuit has 12 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span Mountain View, CA, US, San Diego, CA, US, Oakland, CA, US. Compensation range: $190K - $369K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 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).
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 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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