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About This Role
Overview
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Intuit's Consumer Group (CG) AI is in the business of creating impactful, intelligent experiences that save time and money for our customers. Our work spans two deeply connected fronts: agent\-powered experiences that reason, plan, and act on complex user challenges, and the in\-product recommendation and personalization systems that surface the right guidance, products, and next\-best\-actions to millions of customers at exactly the right moment.
We are looking for a technical leader who is equally at home architecting agent systems and building production recommendation systems grounded in strong classical ML foundations — ranking, retrieval, candidate generation, uplift modeling, and bandits — to drive measurable customer and business outcomes.
If you like designing and deploying ML systems that meaningfully change what customers see and do inside a product — and want to push the frontier on agentic AI at the same time — come, join us.
In this high\-impact role, you will be a key technical leader in architecting and delivering both next\-generation Agentic AI solutions and the in\-product recommendation systems that ensure our customers experience more money, less work, and complete confidence. Responsibilities
- Drive the initiation and design of complex recommendation and agent model solutions. Lead the end\-to\-end architecture and implementation of the team's work, ensuring accountability for high\-quality code, robust design, cost efficiency, and implementation standards.
- Design, build, and deploy in\-product recommendation and personalization systems at scale — including candidate generation, learning\-to\-rank, retrieval, and ranking architectures — that directly drive customer engagement and business metrics.
- Apply both classical and cutting\-edge techniques — including recommendation and ranking systems, gradient\-boosted trees, collaborative filtering, embeddings, Causal\-ML and uplift modeling, multi\-armed bandits, Reinforcement Learning, Online Learning, and Deep Learning — to design and train robust, self\-improving systems on large, real\-world datasets.
- Practice strong leadership and communication skills to influence teams and evangelize the impact of recommendation systems and Agentic AI across the broader organization.
- Partner closely with Product Managers, Software Engineers, and Designers to define problem statements, success criteria, align model metrics with core business goals, and ensure successful delivery and integration of complex ML solutions into the product.
- Work independently and proactively in a fast\-paced environment. Quickly research, explore, and enable new ML, recommendation, and Agentic technologies, staying current with developments in academia and industry to solve Intuit customer problems.
- Develop efficient techniques for designing, evaluating, and continuously improving recommendation and agentic systems through offline and online (A/B, interleaving, bandit) experimentation.
Qualifications
- 8\+ years of industry experience with AI science and machine learning.
- BS, MS, or PhD in Statistics, Mathematics, Computer Science, Economics, Operations Research, or equivalent.
- 7\+ years of hands\-on expertise across core and advanced ML paradigms, including recommendation systems, ranking/retrieval, and classical ML (e.g., gradient boosting, logistic regression, collaborative filtering, feature engineering), as well as Causal\-ML, Reinforcement Learning, Online Learning, and Deep Learning.
- Proven experience building and shipping in\-product recommendation or personalization systems in production, with demonstrable customer or business impact.
- Have extensive prior experience building end\-to\-end, reusable data and model pipelines — from data acquisition through to complex model/agent output delivery — in a production environment.
- Strong business acumen to understand end\-to\-end impact.
Additional preferred skills/experience:* Experience designing large\-scale recommendation systems with two\-stage (candidate generation \+ ranking) architectures and online experimentation
- Experience with multi\-armed bandits, contextual bandits, or uplift modeling for in\-product decisioning
- Experience developing and evaluating complex agentic AI systems
- Experience orchestrating multi\-agent systems
- Authored papers in top conferences and journals on Recommendation Systems, Reinforcement Learning, or Deep Learning
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 \[1] 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 $ 226,000\- 306,000 Southern California $ 211,500\- 286,000 References Visible links 1\. https://www.intuit.com/careers/benefits/full\-time\-employees/
Salary Context
This $226K-$306K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($266K) sits 49% above the category median. Disclosed range: $226K to $306K.
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, AI Product Manager, Data Scientist. 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/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 3,824 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).
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 3,824 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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