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Overview
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The Expert Network's ability to operate efficiently and stay ahead of what's possible for how we serve customers depends on having the right technology infrastructure in place. As our AI \& Technology Enablement lead, you'll own that layer for Center of Excellece — managing the systems and platforms the team runs on, governing the escalation ecosystem that surfaces and resolves critical issues across the organization, and representing the Expert Network's interests in conversations with product and engineering teams about where technology investment should go. You'll also lead COE's readiness as AI capabilities continue to mature within Intuit, building the frameworks that position the Expert Network to adopt new tools effectively as they become available.
This role is well suited for someone who operates comfortably at the intersection of technology and operations — someone who can translate technical complexity into operational clarity, build credibility with product and engineering partners, and see technology not as the end goal but as the lever that enables experts to do their best work.
Responsibilities
- Tech Stack \& Tooling Operations — Manage and govern the full technology stack supporting COE day\-to\-day operations. Partner with IT and product teams for access, integrations, and support. Ensure systems enable rather than create friction for COE teams and the experts they support.
- Escalation Ecosystem — Infrastructure \& Process Design — Own the technology infrastructure and process design for the cross\-org escalation system. Ensure escalations across the Expert Network are tracked, routed, and resolved systematically. This is the infrastructure that makes escalation intelligence visible and actionable for COE leadership and Service Delivery.
- Tech Team Partnerships — Represent COE's interests in product and engineering conversations. Ensure COE needs are reflected in roadmaps and technology investments align with expert experience goals. Build strong working relationships with key product teams early in Year 1 — this role's effectiveness depends on earning a seat in those conversations.
- AI Integration \& Readiness — Stay ahead of Intuit's AI product roadmap as it relates to expert and COE workflows. Develop a readiness framework that positions COE and the Expert Network to adopt AI tools as they mature. Partner with Engagement Governance \& Adoption on adoption; own the technical readiness side.
- Support Function Operations — Manage internal COE support workflows, process documentation, and tooling. Ensure the COE team itself operates efficiently. Own the operational infrastructure that supports COE's internal function.
- Innovation \& Continuous Improvement — Identify opportunities to use emerging tools and technologies to improve COE and expert operations. Test and evaluate new capabilities — particularly AI tools — for applicability to COE's work. Share knowledge with the broader COE team to build collective technical capability.
Qualifications
Experience \& Industry Expertise
- 5–7 years of experience in technology operations, IT operations, product partnerships, business operations, or related fields; experience supporting customer success, service delivery, or large workforce organizations preferred.
- Demonstrated experience managing operational technology stacks, escalation systems, or cross\-functional tech partnerships — with ownership of both the infrastructure and the business outcomes it enables.
- Familiarity with AI/ML tools, Intuit Assist or similar AI platforms, CRM/CSMS tooling, or escalation management systems a meaningful plus.
Educational Background
- We value knowledge and skills built through experience. Whether your background comes from formal education, industry certifications, professional development, or years of doing the work, what matters is the expertise you bring. Relevant domains include technology operations, IT operations, product partnerships, business systems management, or escalation ecosystem design.
Customer\-Centered Mindset
- Understands that technology is only valuable if it enables experts to do the best work of their lives and customers to get the help they need. Evaluates every technology decision through the lens of expert and COE operational impact.
Technical Acumen \& Complex Problem Solving
- Quickly learns and governs tools relevant to COE operations. Applies sound judgment on when to build, buy, or partner. Contributes to AI integration readiness by staying ahead of the tooling roadmap and translating it into a practical COE readiness framework.
- Rapidly diagnoses root causes for operational or technology problems. Uses multiple sources to assess effectiveness and drive improvement. Comfortable with the complexity of a cross\-org escalation ecosystem.
Connect Strategy with Execution
- Connects COE's technology priorities to operational outcomes for all Service Delivery leaders. Ensures the tech stack serves strategic direction and represents COE's needs in product conversations with enough credibility to influence roadmaps.
Leadership \& Collaboration
- Strong cross\-functional partnership with IT, product and engineering teams, Expert Change \& Readiness Management (ECRM), Engagement Governance \& Adoption, and COE leadership. Translates technical complexity to non\-technical audiences. Builds trust with product and engineering partners by being a precise, reliable voice for COE's operational needs.
Execution Orientation
- Proven ability to manage multiple workstreams in a fast\-paced, seasonal environment, delivering measurable business impact. Proactive in anticipating dependencies, risks, and resource gaps, working swiftly to resolve them.
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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.
The expected base pay range for this position is:
San Diego $137,500 \- $186,000
Mountain View, CA $147,500\- $199,500
New York $131,500\- $178,000
Salary Context
This $131K-$199K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($165K) sits 23% below the category median. Disclosed range: $131K to $199K.
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
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 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).
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 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.
Frequently Asked Questions
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