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About This Role
DESCRIPTION
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We are hiring an accomplished Principal, WW Storage GTM Specialist to support the growth of AWS Storage services with a specific focus on high performance object storage workloads. As AWS continues to grow its presence globally, the Principal Specialist will assist our internal teams by creating and orchestrating “at scale” programs, initiatives, research, and enablement for our WW sales and cross functional customer teams.
A proven track record of developing and executing go\-to\-market (GTM) / sales programs and initiatives at scale with tangible results, are critical attributes that will lead to success in the role. A deep knowledge of the analytics market is required.
In this role you will be accountable for:
Setting strategic program direction, as well as oversee tactical execution, of insight based programs and go to market activities with a focus on expanding AWS market share in our top key pursuit accounts, segments, and verticals within the private sector
Using your business savvy to drive revenue, develop sales programs and identify new markets and opportunities. You would work directly with the most interesting and demanding customers to understand their requirements and turn them into reality
Partnering with AWS corporate platform services and industry teams to identify workloads that impact our storage services
A background in mapping the application of technology to customer business scenarios that help our customers understand how to improve their operational outcomes through the use of AWS storage services
Your excellent verbal and written communication skills will allow you build sales enablement tools, evangelize our platform with our enterprise and mid\-market customers as well as our valued partners and serve as the primary Executive Briefing Center representative of the storage business
Act as a trusted business advisor while deep diving into data, creating high impact programs, and tracking progress while tuning for desired results
Be a leader of remote teams. You will guide cross functional teams to drive AWS adoption in the key workloads that use the AWS Storage portfolio
Define, drive, measure, manage and coach on workload focused goals across the business
The ideal candidate will possess both a business background that enables them to drive an engagement and interact at the CxO / VP level, as well as background that enables them to easily interact with IT operations leaders, software developers, and infrastructure and applications architects. He/she should also have a demonstrated ability to think strategically and analytically about business, product, and technical challenges, with the ability to build and convey compelling value propositions, and work across the business to set vision, objectives and to build consensus. A keen sense of ownership, drive, and desire to dive deep is a must.
Key job responsibilities
- Partnering with AWS services and industry teams to identify workloads that impact our storage services
- A background in mapping the application of technology to customer business scenarios that help our customers understand how to improve their operational outcomes through the use of AWS storage services
- Your excellent verbal and written communication skills will allow you build sales enablement tools, evangelize our platform with our enterprise and mid\-market customers as well as our valued partners and serve as the primary Executive Briefing Center representative of the storage business
- Act as a trusted business advisor while deep diving into data, creating high impact programs, and tracking progress while tuning for desired results
- Be a leader of remote teams. You will guide cross functional teams to drive AWS adoption in the key workloads that use the AWS Storage portfolio
- Lead GTM to orchestrate sales motions for sales, marketing, partner, product, training, and professional services teams to accelerate adoption of your aligned technology domain.
- Leverage your deep domain expertise to create best in class field enablement and sales strategy.
- Support field teams in solution building for high priority customers.
- Bring market signals back to product teams to drive innovation on AWS product roadmaps.
- Own reporting and planning cadences to AWS executives on GTM plan execution.
- Demonstrate thought leadership and be able to credibly represent AWS at industry events, conferences, symposiums, etc.
About the team
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS?
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
AWS values curiosity and connection. Our employee\-led and company\-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do.
Mentorship \& Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge\-sharing, mentorship and other career\-advancing resources here to help you develop into a better\-rounded professional.
Work/Life Balance
We value work\-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.BASIC QUALIFICATIONS
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- 7\+ years of developing, negotiating and executing business agreements experience
- 7\+ years of working with Core Cloud Technology Services, including, but not limited to Compute, Edge, Hybrid, Security, and/or Networking experience
- 7\+ years of Go\-To\-Market, Business Development, Sales, or Consulting experience
- 7\+ years of working with Advanced Compute technologies including, but not limited to: Accelerated Compute, High Performance Compute, Visual/Spatial Compute, and/or IoT. experience
- 7\+ years of working with Enterprise Application Modernization and Migration technologies, including, but not limited to, Mainframe, Serverless, Containers, or Cloud Operations experience
- 7\+ years of working with Data \& AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage experience
- Experience developing strategies that influence leadership decisions at the organizational level
PREFERRED QUALIFICATIONS
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- Experience as a founder or executive focused on related segments, or as a practice leader or business unit owner
- Experience selling to Fortune 1000 or Global 2000 organizations
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how\-we\-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign\-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life \& AD\&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, MA, Boston \- 176,100\.00 \- 238,200\.00 USD annually
USA, NY, New York \- 193,700\.00 \- 262,000\.00 USD annually
USA, WA, Seattle \- 176,100\.00 \- 238,200\.00 USD annually
Salary Context
This $176K-$262K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Amazon Web Services, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $176K to $262K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Amazon Web Services AI Hiring
Amazon Web Services has 73 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist, Data Scientist. Positions span New York, NY, US, Austin, TX, US, Jersey City, NJ, US. Compensation range: $129K - $342K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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