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About This Role
Overview
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The hardest part of shipping AI powered products is not writing the code. It is everything that comes after: deploying safely, observing what services and agents actually do in production, evaluating quality, and staying resilient at scale. Intuit is investing heavily in exactly this layer, and we are hiring a Staff Technical Product Marketing Manager to own how these capabilities reach, win over, and empower thousands of Intuit developers.
You will join the Technical Product Marketing and Developer Advocacy team inside Developer Relations, owning go to market for Intuit's backend and AI platform offerings. On the backend side, this spans Intuit Kubernetes Service, observability, incident management, and serverless paved roads. On the AI side, it spans the GenOS evaluation platform, AI agent paved roads, and model playgrounds built by our AI Foundations teams. These platforms power Intuit's customer\-facing AI experiences across TurboTax, QuickBooks, Credit Karma, and Mailchimp, so how quickly internal teams adopt them directly shapes how fast Intuit ships AI to customers.
This is a builder role for a technical storyteller. You will drive internal product launches and end to end narratives, grow adoption in partnership with developer advocates, community managers, and technical writers, and represent your product areas externally through conference talks, blogs, and industry thought leadership. You will also help power Intuit's push to make every engineer an AI native builder by helping backend engineers mature through our AI proficiency framework and reach our AI adoption goals.
The ideal candidate combines genuine technical depth in cloud native infrastructure and the modern AI stack with the craft to turn complex platform capabilities into narratives that move developers to act. Responsibilities
- GTM ownership: drive go to market strategy and execution for internal backend platform offerings (Kubernetes based hosting, observability, incident management, serverless paved roads) and AI platform offerings (evaluation tooling, AI agent paved roads, model playgrounds)
- Launches: own product launches end to end, from positioning and messaging through launch plans, enablement assets, and post launch adoption measurement
- Narratives: build durable stories that connect individual capabilities into a coherent picture of how developers deploy, operate, and improve AI powered services at Intuit
- Adoption: grow usage and reduce onboarding friction in partnership with developer advocates, community managers, and technical writers
- Thought leadership: shape Intuit's external presence in AI infrastructure and developer platforms through conferences, blogs, and industry engagement
- AI proficiency: help Intuit engineers progress through Intuit's AI proficiency framework and contribute to company wide AI adoption goals
- Voice of the developer: gather and operationalize developer feedback with platform product managers to influence roadmaps
- Measurement: define and report adoption, awareness, and engagement metrics for your product areas
Qualifications
- 8\+ years in product marketing, technical marketing, developer relations, or adjacent technical GTM roles, with demonstrated staff level scope and influence
- Solid understanding of cloud native infrastructure: Kubernetes, observability (metrics, logs, traces), CI/CD and deployment practices, and resiliency or SRE concepts
- Working fluency in the modern AI stack: LLMs, agents, evaluation frameworks, and model operations, with hands on curiosity for the tools you market
- Experience marketing to developers or driving platform adoption at scale, with deep empathy for developer workflows and day to day needs
- Confident articulating value propositions to executives, yet able to seamlessly drop into technical details with staff engineers
- Experience creating multi channel campaigns and assets that support launch and sustained adoption of technical products
- Excellent written and verbal communication; comfortable presenting to large technical audiences
- External presence (conference talks, technical blogs, open source or community engagement) strongly preferred
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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.
Bay Area: $188,000 \- $240,000
The expected base pay range for this position is:
Mountain View $188,500 \- $255,000
Salary Context
This $188K-$255K range is above the 75th percentile 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 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $188K to $255K.
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
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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