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About This Role
DESCRIPTION
The AWS Data & AI Partner GTM team supports the world's most innovative partners - from breakthrough startups to Large Language Model Providers (LLMs) to global tech giants - helping them envision and build the future of autonomous AI & Data systems. The WW Partner GTM Specialist for Data will be the driving force behind AWS's partner strategy for joint go to market with ISVs and Consulting partners building Data strategies and architectures, AI-ready data infrastructure, and autonomous agent frameworks. Your strategies will shape how businesses evolve in the age of AI agents, your insights will influence product directions at AWS, and your vision will help partners transform their business models for the AI & Data era.
This role sits at the intersection of breakthrough technology and business transformation. You'll pioneer new business models in the rapidly evolving AI agent space while working with the most sophisticated AI technologies and partners globally. As a key strategist, you'll shape how AWS and our partners approach autonomous AI systems, building programs that will scale Agentic AI adoption worldwide. Your thought leadership will help define one of technology's most dynamic spaces, influencing how businesses across industries implement and benefit from AI agents.
If you're excited about being at the forefront of the AI revolution and want to make a lasting impact on how businesses harness autonomous AI agents, this role offers you the platform, the resources, and the opportunity to shape the future.
Key job responsibilities
- Collaborate with AWS Data partners, helping them envision and implement Semantic data solutions support autonomous agents and that deliver exceptional customer value. Your insights will shape both partner strategies and AWS's approach to this transformative technology.
- Drive strategic initiatives to accelerate Semantic data and and agentic adoption with a core set of strategic partners
- Develop and execute comprehensive partner strategies that scale data-based solutions and use cases
- Build and manage programs for partner enablement, certification, and funding focused on agent technologies
- Analyze market trends and gather partner/customer feedback to inform Semantic data strategy
- Create scalable frameworks that enable partners to successfully build and deploy agent-based solutions
- Collaborate with AWS service teams to align partner requirements with product development
- Lead the development of partner-ready go-to-market content, resources, and activities
A day in the life
- Develop AWS strategy for Data partner GTM
- Drive consensus across service teams, field sales, partner management, marketing, legal, and partner program teams.
- Conduct analysis on AWS customer and partner adoption trends
- Analyze performance metrics and prepare MBRs, 2x2 for leadership review. Coordinate with various teams to ensure alignment on deliverables and timelines.
- Build customer and field facing enablement materials including sales plays, decks, and solution briefs
About the team
The AWS Data & AI Partner GTM team isn't just building partnerships - we're creating the future of intelligent systems through an elite network of innovators. We're the driving force behind AWS's most strategic partner initiatives across data, AI, and autonomous agents, bringing together the world's most innovative partners to pioneer transformative solutions.
Our vision is bold: we're building the world's premier Data & AI partner network, where technical excellence drives remarkable innovation. From helping enterprises unlock the value of their data to advancing the frontier of AI, we champion emerging technologies that reshape how businesses operate. Our team works at the intersection of partner innovation and AWS's most strategic initiatives, from data foundations to autonomous agents, creating opportunities that define the future of business technology.
What makes our team unique - we're not just enabling partner success, we're shaping the future of data and AI adoption. We work directly with consulting partners, category-defining ISVs and startups as well as AWS' most innovative service teams. Through our work, we're not just witnessing the data and AI revolution - we're actively driving it, creating scalable models that empower both our partners and AWS field teams to accelerate service adoption and deliver exceptional customer outcomes. Whether it's helping partners build advanced data analytics solutions or develop sophisticated AI applications, we're at the forefront of technology's most exciting frontiers.BASIC QUALIFICATIONS
- 5+ years of developing, negotiating and executing business agreements experience
- 5+ years of Go-To-Market, Business Development, Sales, or Consulting experience
- 5+ years of working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage experience
- 5+ years of in program management, workforce strategy development, or metrics-driven decision making experience
- Experience developing strategies that influence leadership decisions at the organizational level
- Experience managing programs across cross functional teams, building processes and coordinating release schedules
- Experience selling enterprise software or cloud-based applications
- Experience presenting to both technical and non-technical executive audiences
- Experience leading complex, multi-year initiatives that may be cross-functional and/or span business and technology
PREFERRED QUALIFICATIONS
- Master's degree in business, data science, public administration, finance, engineering, human resources, or related field, or PMP certificate
- Experience interpreting data and making business recommendations
- Experience identifying, negotiating, and executing complex legal agreements
- Experience designing, implementing, and scaling workforce development programs across large-scale operations, including forecasting workforce needs and leveraging analytics to drive program decisions
- Experience interpreting data and making business recommendations across leadership and cross-functional teams
- Experience using analytical tools for workforce metrics and reporting
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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, CA, Irvine - 147,900.00 - 200,100.00 USD annually
USA, CA, San Francisco - 162,700.00 - 220,200.00 USD annually
USA, NY, New York - 162,700.00 - 220,200.00 USD annually
USA, WA, Seattle - 147,900.00 - 200,100.00 USD annually
Salary Context
This $147K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($184K) sits 13% below the category median. Disclosed range: $147K to $220K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Amazon Web Services AI Hiring
Amazon Web Services has 71 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Engineer, Data Scientist. Positions span New York, NY, US, Bellevue, WA, US, Denver, CO, US. Compensation range: $97K - $299K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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