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About This Role
DESCRIPTION
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Amazon seeks an experienced Customer Success Manager / Strategic Accounts Manager (based in Seattle) to join our Agentic Payments Customer Success team.
Senior Customer Success Managers own an expanding book of Large Enterprise Customers, with whom they consult and provide guidance on Agentic Payments enablement. This customer\-facing role requires collaboration with C\-Suite partners to identify optimal agentic commerce enablement paths, informed by in\-depth knowledge of payment solutions, API and catalogue integrations delivering the best customer experience. Agentic Commerce is an evolving, rapidly expanding space, so we seek an innovative, creative, commercially agile Account Management professional excited by innovation, while fiercely Customer Obsessed in delivering optimal shopping experiences.
The successful candidate will be expert at delivering results via collaborative relationships. They excel in identifying and enabling Customer objectives related to payment solutions and on\-site experiences. They provide insights and guidance on strategies best addressing each Customer need. They are data led, monitoring performance to flag anomalies and suggest improvements, and work across Amazon teams to surface new opportunities for their e\-commerce partners.
Key job responsibilities
Strategic Advisory: Executive alignment and C\-level stakeholder management; bespoke account planning; aligning customer roadmaps with the agentic payments product roadmap.
- Accountable for establishing and retaining C\-Suite Customer relationships via proactive relationship management and advisory services.
- Build Customer account plans informing strategy, annual planning and contract renegotiations.
- Provide guidance and support on customer experience optimization.
Agentic Payment Enablement: Customer Success Managers provide strategic guidance and consultative oversight on Agentic Commerce enablement, serving as the primary advisor to enterprise customers on adoption pathways, agentic payment workflow options, and performance optimisation — coordinating with internal technical teams to ensure seamless delivery.
- Serve as the primary point of guidance for customers navigating payment solution integrations, coordinating with Amazon technical and product teams to ensure delivery standards are met.
- Monitor performance of autonomous transaction workflows and escalate issue resolution.
Performance Monitoring: Conducting QBRs and executive business reviews; tracking KPI payment performance metrics (transaction volume, success rates, usage velocity); monitoring conversion
- Ensure Customer retention with proactive performance management, using AI\-powered health scoring systems analysing transaction patterns, output metrics and payment success rates.
- Troubleshoot performance issues with deep dive analysis leveraging AI\-powered customer health scoring and predictive churn modelling.
- Provide oversight on "pilot\-to\-production acceleration", tracking onboarding progress and monitoring "agents in production" to ensure seamless customer transition.
Expansion and Growth: Identifying growth opportunities; driving payment volume growth; surfacing expansion opportunities across new agentic workflows
- Explore new market\-segment opportunities focused on value optimization and expansion initiatives.
Cross Functional Coordination: Partnering with Product, Engineering, Sales, and Research teams; acting as voice of the customer; funnelling product requirements and field patterns to internal teams
- Represent Customers internally, collaborating to ensure high integration standards, payment success rates and new opportunities.
- Ensure balanced judgement in assessing client needs / internal priorities.
A day in the life
Your day starts with the weekly connect of your team across Seattle, London, Munich and Luxembourg. You share progress made on your portfolio last week, including new insights uncovered in a recent agentic discovery call. The team is updated on a recently launched agentic protocol and resulting opportunities for customers. You join a call with the Solutions Architect supporting one of your Merchant's catalogue integrations and you align on next steps for today's update with the Merchant's tech resource. You have an introduction call with a merchant new to agentic commerce, you share industry insights as well as Amazon opportunities and they agree to move forward. You update Salesforce and 'Voice of the Merchant' records, capturing Merchant insights.
About the team
Amazon Agentic Payments functions as a payment acceptance organization providing foundational payment capabilities for merchants of all sizes. Our mission is to deliver trusted, seamless payment experiences customers love.
BASIC QUALIFICATIONS
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- Bachelor's degree or equivalent
- Experience proactively growing customer relationships within an account while expanding their understanding of the customer's business
- Experience identifying, developing, negotiating, and closing opportunities across a wide spectrum of customer engagement levels
- Experience positioning and selling innovative solutions to new and existing customers and market segments
PREFERRED QUALIFICATIONS
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- 5\+ years of B2B or enterprise sales with a focus on hunting new business experience
- Experience identifying trends and needs to improve an already closed large\-scale technology deal
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, WA, Seattle \- 113,100\.00 \- 160,000\.00 USD annually
Salary Context
This $113K-$160K range is in the lower quartile 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 Amazon.com, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($136K) sits 25% below the category median. Disclosed range: $113K to $160K.
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.
Amazon.com AI Hiring
Amazon.com has 102 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, AI Product Manager, Data Scientist. Positions span New York, NY, US, Palo Alto, CA, US, Bellevue, WA, US. Compensation range: $129K - $300K.
Location Context
AI roles in Seattle pay a median of $227,400 across 1,084 tracked positions. That's 14% 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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