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About This Role
Overview
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About Intuit
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Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe everyone should have the opportunity to prosper. Through QuickBooks Advanced and the Intuit Enterprise Suite (IES), we are extending the power of our platform into the mid\-market — and reimagining what customer onboarding looks like in an AI\-first world.
The Opportunity
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Intuit is investing decisively in mid\-market growth. QuickBooks Advanced already serves hundreds of thousands of mid\-market customers, and Intuit Enterprise Suite is on a trajectory to reach tens of thousands of new customers in the coming year. This role sits at the leading edge of that investment: defining how AI transforms onboarding for complex businesses, and shaping a service model that scales economically across both programs.
This is high\-velocity, high\-impact work. You'll partner with Product, AI/ML, and Service Design leaders to determine where AI takes the lead in the customer experience, where it augments expert ICs and CSMs, and where humans remain at the center. You'll have direct line of sight to SVP\-level decisions and the latitude to move quickly — making weekly decisions, running rapid experiments, and shaping the trajectory of a strategic growth program.
If you want to build the next generation of AI\-enabled customer experience for one of the most ambitious software platforms in the world, this is the role.
Responsibilities
What You'll Own
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- Design the AI\-enabled service model. Define where AI is the primary experience, where it augments human ICs and CSMs, and where expert\-led service remains the lead. Translate Intuit's AI and product roadmap into a coherent service strategy across QBA and IES.
- Lead the strategic onboarding vision. Set the experience strategy for mid\-market onboarding end to end — from first touch through activation, adoption, and retention. Partner with Service Design and Service Delivery to bring it to life.
- Make the business case. Quantify the impact of AI\-enabled service offerings on cost\-to\-serve, time\-to\-value, retention at days 30/60/90/365, and feature adoption. Translate design decisions into measurable outcomes the executive team can act on.
- Shape the product roadmap. Partner with Product, AI/ML, and design partners to influence in\-product experiences so that service and product evolve as one integrated system.
- Move with velocity. Run pilots, learn fast, and scale what works. Recent pilots have delivered measurable retention gains and meaningful reductions in professional services case volume — and we're just getting started.
Qualifications
Who You Are
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- A 0\-to\-1 builder. You've designed and launched a customer experience program, service offering, or AI\-enabled product capability — and shipped it to real customers. You move with confidence when the path is unfamiliar.
- AI\-fluent. You read AI and product roadmaps and immediately see service implications. You've translated emerging AI capabilities into customer experience design decisions, and you hold your own in technical conversations with ML and product leaders.
- A systems thinker with commercial instincts. You think in flywheels: how an AI\-enabled experience changes cost\-to\-serve, which changes pricing, which changes attach, which changes retention. You can model the second\-order effects.
- A storyteller with evidence. You synthesize quantitative and qualitative signal into narratives that move executives to act. Your decks land. Your recommendations travel without you in the room.
- A magnetic collaborator. You build trust across Product, Analytics, Sales, Service Delivery, and Service Design. You know when to push, when to listen, and when to commit.
What You Bring
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- Demonstrated experience designing and launching a customer experience, service offering, or AI\-enabled product capability with measurable business impact.
- Track record of translating AI and ML product capabilities into service or experience design decisions.
- Strong quantitative and analytical foundation; comfort working directly with data, not just consuming it secondhand.
- Executive presence — you can carry a recommendation to a VP or SVP audience and defend it with evidence.
- Bachelor's degree in a business, technical, or science discipline; MBA or advanced degree preferred or equivalent operating experience.
- Experience with Financial Management Software (QuickBooks, Sage Intacct, NetSuite) is a plus.
Why This Role Matters
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Mid\-market is one of Intuit's most significant growth opportunities, and AI\-enabled customer experience is one of the most strategic levers we have to win it. The person in this role will help define what world\-class onboarding looks like for a market segment with rising expectations and increasing complexity — and will shape an experience that the rest of the industry measures itself against.
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Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will 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:
Mountain View, CA $207,000\- $280,000
San Diego, CA $185,000\- $250,000
Salary Context
This $185K-$280K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Intuit, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($232K) sits 6% above the category median. Disclosed range: $185K to $280K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Intuit AI Hiring
Intuit has 10 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Product Manager. Positions span San Diego, CA, US, Mountain View, CA, US, New York, NY, US. Compensation range: $251K - $284K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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