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About This Role
DESCRIPTION
Outbound is re-imagining the communications and marketing landscape with generative and agentic AI, changing how millions of customers discover products and engage with brands across Amazon.com and beyond.
Amazon reaches shoppers billions of times through email, mobile push, SMS, and other messaging channels. We're turning that into a marketplace. Each shopper's limited attention is the supply. The messages that compete for it, and the marketers, category leaders, and AI agents who create them, are the demand. A separate team runs the auction that decides which message earns each moment. This role builds the demand side: everything that determines how good, how varied, and how dependable the content and campaigns entering that marketplace are.
Today, most marketing email still looks like a catalog printout, and the tools behind it make ambitious creative slow and fragile. We want Amazon's messages to have the visual quality of the best brand content on social, and to be relevant to the specific moment a customer is in. A customer getting into cycling, or planning a first camping trip, should get a story-led series built for that interest, not a generic deal alert. Picture a feed: AI agents continuously generate on-brand, category-specific content from customer and category signals, and the marketplace decides what each customer sees and when. Your team makes that inventory rich, high quality, and trustworthy.
As the Senior Manager, Software Development for Generative Marketing & Demand, you'll build and lead an organization of software engineers, working closely with applied scientists and design technologists. You'll own:
- The authoring platform (open source). A toolkit that takes a design from Figma, a brief in Slack, or a contributor's draft and produces finished, accessible, on-brand content for email, push, and long-form article pages. It covers a shared component and design-token spec, a compiler that produces inbox-safe output that renders as designed in every client, automated checks (accessibility, size, links, deliverability, brand rules), real-client previews, and in-thread review and approval. We intend to build the core in the open under a permissive license, so the industry's best email and design-tool engineers can improve it alongside us, while Amazon's customer data, brand, and sending systems plug in privately through adapters.
- Generative and agentic content. Agents that draft, adapt, and localize creative, generate lifestyle imagery for catalog products, write copy in a brand's voice, and assemble category-specific stories and drip series from customer and category signals and from opportunities the marketplace sends back. Every generated piece passes the same gates as human work before it reaches a customer.
- Campaign planning and execution. The tools marketing teams use to plan, configure, schedule, and operate campaigns and coordinated waves of campaigns, including peak events like Prime Day. The bar is simple: what a marketer plans is what customers get. Operators see the effective send window, the policies that apply (quiet hours, cadence, consent, channel limits), and a reconciled funnel from audience to delivery, with the reason behind every number, before and during a send rather than in a retro afterward.
- Data and audience connectors. A pluggable, documented mechanism for bringing in audiences, catalog and deal data, recommendations, and third-party sources, with timeouts, fallbacks, and validation, so new data sources can be added without platform changes. Connectors built by Amazon teams and by the open-source community use the same contract.
- Send-path reliability. Low-latency, high-throughput content rendering and handoff to Amazon's sending infrastructure at peak-event volumes, with verifiable delivery: every send is traceable, auditable, and reconciles with what was planned.
Key job responsibilities
- Build, hire, and grow a high-performing team of software engineers, including senior and principal engineers and, as the organization scales, managers. Set a high hiring bar and develop the next generation of technical leaders.
- Own the Generative Marketing & Demand vision, roadmap, and multi-year plan (OP1/OP2) with product, design, science, and marketing partners. Turn an ambiguous goal, best-in-class creative and campaign tooling in an industry with strong commercial incumbents, into a delivery plan with measurable milestones.
- Lead the open-source program for the authoring platform. Work with Amazon's open-source office and Legal on licensing and the open/private boundary. Build the community mechanisms that make it a multi-party project rather than an Amazon-only one: public roadmap, RFC process for spec changes, contribution and code-review standards, conformance tests, release management, security response, and named maintainers inside and outside Amazon. Recruit external maintainers and early adopters (other companies, agencies, ESPs, and design-tool developers), and measure the project's health by external contributors and downstream adoption.
- Hold a clear architectural line between open and private: nothing in the open core depends on Amazon systems, no customer data flows through open code paths, and Amazon runs the released open packages rather than a fork.
- Be accountable for outcomes. Creative quality and relevance (engagement, conversion, and incremental lift against holdouts), brief-to-approved-content time, rendering and accessibility defects, plan-versus-actual send accuracy, and marketer adoption are the measures. Opens alone are not.
- Partner with applied scientists on content generation, creative quality scoring, category and interest modeling, and the signals that tell agents which content to create for which customer moment. Move models from research to production quickly and safely.
- Partner with the marketplace team so that content, metadata, and quality signals flow cleanly into the attention auction, and opportunity signals flow back to content agents.
- Make AI-generated content safe by default: brand, legal, claims, accessibility, and deliverability checks block publishing automatically; human reviewers focus on judgment; prompts and outputs are logged; and responsible-AI, privacy, and communications compliance requirements (CAN-SPAM, TCPA, GDPR, consent) are non-negotiable.
- Drive adoption across a broad set of internal tenants, including Retail, Prime, Prime Video, Music, Alexa, and Ads marketing teams, and category leaders who contribute long-form content. Meet them in the tools they already use (Figma, Slack, documents, CLI, and agent clients via MCP) rather than asking them to learn a new console.
- Own operational excellence: on-call health, alarming, COEs, security reviews, service-level goals, and peak-event readiness, including during Prime Day, Prime Big Deal Days, and the holidays.
- Communicate clearly in writing and in reviews with senior leadership, including narratives, PR/FAQs, and business reviews, and represent Amazon externally in the open-source and email-engineering communities.
A day in the life
You might start with the open-source triage: an external contributor's pull request fixes a dark-mode rendering bug in a new mail-client release, and another company has proposed an RFC to extend the spec for interactive modules. Then you'd review the weekly metrics: a category storytelling pilot for outdoor gear is showing incremental new-to-category customers against its holdout, and brief-to-approved time dropped below a day. After that, you'd walk a marketing lead through a readiness review for the next peak event, where the wave view shows every campaign's effective send window and flags one that a channel quiet-hour rule will cut short, so it gets fixed before the event instead of during it. In the afternoon, you'd join a design review for a new connector contract with your principal engineer, meet with an applied scientist on why generated lifestyle imagery is drifting off-brand for one category, and close an offer for a senior engineer who found the team through the open-source project.
About the team
We're a high-ownership team in Outbound Communications, building the tools that decide how good Amazon's messages can be. We build from first principles, work in the open where it makes the product better, and hold to one rule: every message has to be worth the customer's attention.BASIC QUALIFICATIONS
- 10+ years of engineering experience
- 5+ years of engineering team management experience
- 10+ years of planning, designing, developing and delivering consumer software experience
- Experience partnering with product or program management teams
- Experience managing multiple concurrent programs, projects and development teams in an Agile environment
PREFERRED QUALIFICATIONS
- Experience partnering with product and program management teams
- Experience designing and developing large scale, high-traffic applications
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, NY, New York - 242,100.00 - 327,500.00 USD annually
USA, WA, Seattle - 220,100.00 - 297,700.00 USD annually
Salary Context
This $220K-$327K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2094 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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% 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 $212,160 based on 13,809 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($273K) sits 29% above the category median. Disclosed range: $220K to $327K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Amazon.com AI Hiring
Amazon.com has 68 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, Data Scientist, AI Product Manager. Positions span New York, NY, US, Seattle, WA, US, San Diego, CA, US. Compensation range: $148K - $327K.
Location Context
AI roles in New York pay a median of $221,000 across 3,262 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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