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### Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 9 years of experience in marketing, including management consulting, digital transformation, or product marketing.
- Experience in management consulting, digital transformation, or a related field, including experience in a client\-facing leadership capacity.
- Experience with Artificial Intelligence (AI) and Machine Learning (ML) tools.
- Experience in project, product, or technical program management.
### Preferred qualifications:
- Ability to connect the dots across teams, insights and initiatives with a high tolerance for ambiguity and the ability to work in a fast\-paced environment.
- Strong problem\-solving and experience working with and analyzing data with the ability to distill analysis to simple communications.
- Interest in marketing and a passion for consumer tech, AI and research.
- Strong track record of delivering projects and managing multiple projects simultaneously.
- Strong leadership, influence, and stakeholder management skills.
- Excellent written and verbal communication, spreadsheet software, presentation design and people skills.
About the job
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In this role, you will serve as the strategic and creative engine for the Consumer Shopping, Retail and U.S. Ads Marketing teams, partnering with Marketing leaders across a vast portfolio, from frontier AI initiatives to billion\-user products. You will drive product marketing, core strategy, research and insights, partnerships and special projects. Your mission is to architect the strategic frameworks and marketing solutions to help drive adoption of Google Products in the consumer and merchant ecosystems.
In this role, you will act as the central architect for the AI\-first future across Shopping, Retail and US Ads marketing, reporting to and working closely with the Commerce Executive Director. As a part of this responsibility, you will have full autonomy to identify the opportunity space, define the strategy, and drive the execution of AI transformation, bringing all marketers, agencies, and core ways of working into the age\-of\-AI. You will manage planning and operational processes and work cross\-functionally with key partners to solve our toughest issues, advocate for high\-priority projects, and drive greater operational excellence within Marketing.
In this role, you will bring an AI native mindset with deep familiarity in current AI trends, tools, and methodologies, identifying opportunities for AI integration, designing innovative solutions, and leading their implementation to drive significant business transformation. You must also be comfortable taking a hypothesis\-driven approach to problem\-solving, have effective quantitative and communication skills, and the ability to take on broad\-reaching and ambiguous questions while working collaboratively and cross\-functionally with Googlers of all levels.Know the user. Know the magic. Connect the two. At its core, marketing at Google starts with technology and ends with the user, bringing both together in unconventional ways. Our job is to demonstrate how Google's products solve the world's problems\-from the everyday to the epic, from the mundane to the monumental. And we approach marketing in a way that only Google can\-changing the game, redefining the medium, making the user the priority, and ultimately, letting the technology speak for itself.Individual pay is determined by factors including job\-related skills, experience, and relevant education or training.
US: $171000 \- $248000 (USD) \+ 20% bonus target \+ bonus \+ equity \+ benefits
Learn more about benefits at Google.Responsibilities
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- Collaborate with the Commerce Executive Director and Strategy and Operations (S\&O) Lead to deliver and execute AI transformation strategy for Shopping, Retail and US Ads Marketing, identifying and prioritizing core\-business areas with the greatest potential for AI\-driven disruption and efficiency.
- Develop the strategic roadmap and business\-case for transformation, articulating a goal of future\-state and quantifying the expected Profit and Loss (P\&L) impact through increased\-productivity and reduced operational\-cost.
- Own the team Objectives and Key Results (OKR) to rapidly increase Marketers’ AI proficiency of Google’s tools, continuously finding ways to educate and surface new possibilities into workflows.
- Rethink processes from first principles to use AI to replace legacy\-workflows, improve co\-ordination, and accelerate and improve output.
- Drive the goal, strategy, and execution across various Marketing functions for their AI transformation, serving as the primary strategic\-operator for horizontal strategic\-projects across Commerce, Retail and U.S Ads with AI\-first workflows.
Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.
Salary Context
This $171K-$248K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Google, 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($209K) sits 16% above the category median. Disclosed range: $171K to $248K.
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.
Google AI Hiring
Google has 155 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist, AI Safety. Positions span Mountain View, CA, US, Reston, VA, US, Raleigh, NC, US. Compensation range: $151K - $428K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national 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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