Interested in this AI/ML Engineer role at Google?
Apply Now →Skills & Technologies
About This Role
In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:
- Health, dental, vision, life, disability insurance
- Retirement Benefits: 401(k) with company match
- Paid Time Off: 20 days of vacation per year, accruing at a rate of 6\.15 hours per pay period for the first five years of employment
- Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
- Maternity Leave (Short\-Term Disability \+ Baby Bonding): 28\-30 weeks
- Baby Bonding Leave: 18 weeks
- Holidays: 13 paid days per year
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Sunnyvale, CA, USA; Kirkland, WA, USA.### Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 11 years of experience in management consulting, product management and strategy, or analytics in a technology company.
- Experience working with and analyzing data, and managing multiple cross\-functional programs or projects.
- Experience with financial forecasting, financial modeling.
- Experience in go\-to\-market, product strategy/planning, program management.
### Preferred qualifications:
- Advanced degree or equivalent practical experience.
- Experience with R, SQL, spreadsheets, or similar analytics tools.
- Experience working with product and engineering teams.
- Excellent written and verbal communication skills.
About the job
-----------------
Product and Business Strategy Leaders bring together teams across Google’s functions to help products execute optimally. Our team pushes Google to scale at key points that refine our products and infrastructure by executing efficiently, bringing solid business sense and sound judgment, and working effectively across organizational lines.
Our roles often include components of strategy (e.g. analyzing and understanding new trends in the industry, building business plans), operations (e.g. running the cadence of organizations, connecting the operating lines between our functions), and communications. Our team partners with senior leadership to run important functions that cross\-cut our existing organizations and deliver high impact projects. We help Engineers, PMs, UX, and all of our other functions to build amazing products that delight our users, and then get those products into their hands.
The Product Strategy and Operations team sits at the intersection of innovation and execution, guiding the trajectory of our most impactful initiatives. We operate in a highly dynamic and ambiguous space, requiring adaptable thinkers who can continuously evolve and drive clarity without relying on a predefined playbook.
We embrace complex issues and pivot quickly alongside shifting industry landscapes, ensuring that our products and foundational infrastructure remain firmly.
In this role, you will leverage deep investigative insights to shape the future of our overarching product offerings. This role is critical for translating complex, ambiguous data into decisions that ensure our solutions remain impactful on a global scale. Your work will directly guide product roadmaps, empowering cross\-functional teams to optimize performance, refine user experiences, and drive sustainable growth across rapidly evolving markets.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise\-grade solutions that leverage Google’s cutting\-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.Individual pay is determined by factors including job\-related skills, experience, and relevant education or training.
US: $176000 \- $256000 (USD) \+ 20% bonus target \+ equity \+ benefits
Learn more about benefits at Google.Responsibilities
--------------------
- Shape foundational product strategy by designing and deploying advanced investigative frameworks that uncover actionable user insights and market trends.
- Synthesize complex, unstructured data sets into clear, recommendations for leadership and cross\-functional partners.
- Develop metrics, serve insights agentically, and build predictive models that identify structural growth opportunities and resolve potential bottlenecks before they manifest.
- Conduct rapid iteration, A/B testing, and experimentation to validate the business impact of proposed product solutions.
- Build and maintain scalable analytical models and dashboards, ensuring the highest standards of data quality and reliability for enterprise\-wide decision\-making.
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 $176K-$256K 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 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 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: $176K to $256K.
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.
Google AI Hiring
Google has 134 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, AI Agent Developer, Research Scientist. Positions span Sunnyvale, CA, US, New York, NY, US, Mountain View, CA, US. Compensation range: $144K - $365K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.