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Overview
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Intuit’s AI Transformation Org is changing how 18,000\+ employees do their most important work. We bring AI agents into the everyday workflows of Finance, Legal, Marketing, Customer Success, and People \& Places. The people doing the work spend less time on coordination and more time on the decisions that need their expertise. We need a Principal who can scale this across the company without taking it over.
This role is the engine that gets AI\-redesigned workflows into production and keeps them there. You partner with AI Champions inside each non\-engineering function. You apply approved enterprise blueprints to high\-friction, repeatable workflows. You set measurable baselines before redesign. You make adoption stick after the central team steps back. Your success is measured by what the function can do after you leave, not by what you personally redesigned.
You partner closely with the People \& Places AI Workforce Transformation team, the Enterprise Solution Architect, the Telemetry and Insights Lead, and senior operating leaders across Intuit’s most consequential non\-engineering functions.
Responsibilities
- Drive blueprint\-led redesign with AI Champions across non\-engineering functions. Apply approved blueprint patterns to the workflows we prioritize. Coach Champions through configuration, integration, evaluation, and rollout in their real work, not in training settings.
- Hold the line on blueprint standards. Every redesign uses approved workflow logic, agent configuration standards, integration patterns, governance guardrails, and measurement contracts. Push back on bespoke designs that fragment the enterprise architecture.
- Design redesigns to stick. Set baselines before launch. Instrument the pre\-state with the Telemetry and Insights Lead. Align role expectations and habits with HR Craft Leaders. Validate the post\-state. Treat behavioral adoption as a design constraint from day one, not a retrofit.
- Drive blueprint adoption, not workflow possession. The redesigned workflow is the default execution model in production. The function owns it after you step back. The hand\-off is the deliverable, not the redesign itself.
- Surface friction back to platform and architecture. Identify where blueprint patterns break down, where the platform is missing capability, and where governance creates drag. Feed signal to the Enterprise Solution Architect and the platform engineering team. The platform should compound with every redesign.
- Connect workflow redesign to workforce evolution. Every redesign changes what the people doing the work need to know, do, and decide. Partner with HR Craft Leaders and the People \& Places AI Workforce Transformation team. Role expectations, AI proficiency, hiring criteria, and learning pathways evolve with the workflow, not after it.
- Maintain the redesign portfolio view. Make redesigns in flight, baselines committed, value captured, and risk visible at any moment. Keep prioritization honest. Resist pressure to take on work that does not meet the bar.
Qualifications
QUALIFICATIONS
Required
- 12\+ years of progressive experience designing, building, or operating production AI/ML systems, agentic workflows, or enterprise workflow platforms.
- At least 3 years driving change inside other people’s organizations. This could be embedded with a business function, a forward\-deployed engineering team, or a transformation function.
- Demonstrated track record of taking AI\-powered workflows from prototype into in\-production adoption at enterprise scale.
- Experience influencing and aligning VP\-level and above stakeholders on workflow strategy and investment decisions.
- Bachelor’s degree required. Computer Science, Engineering, or a related technical field preferred.
Preferred
- Master’s degree in Computer Science, Engineering, or a related technical field.
- Background in enterprise HR, Finance, Legal, Marketing, or Customer Success technology. These are the functions where this role will spend most of its time.
- Experience designing or implementing responsible AI frameworks, evaluation pipelines, or AI governance programs.
- Prior experience as a forward\-deployed engineer or AI solutions architect inside a customer’s organization.
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:
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 in Demand for This Role
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 $181,170 based on 12,692 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 $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Intuit AI Hiring
Intuit has 13 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Data Scientist, AI Software Engineer. Positions span San Francisco, CA, US, Mountain View, CA, US, San Diego, CA, US. Compensation range: $190K - $357K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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